{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listed Volatility and Variance Derivatives\n",
    "\n",
    "**Dr. Yves J. Hilpisch &mdash; Wiley Finance (2016)**\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/lvvd_cover.png\" alt=\"Derivatives Analytics with Python\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Basics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 3  # defining an integer object\n",
    "type(a)\n",
    "\n",
    "a.bit_length()  # number of bits used\n",
    "\n",
    "b = 5.  # defining a float object\n",
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "333"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = 10 ** 100  # googol number\n",
    "type(c)\n",
    "\n",
    "c # long integer object\n",
    "\n",
    "c.bit_length()  # number of bits used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "243.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 / 5.  # division\n",
    "\n",
    "a * b  # multiplication\n",
    "\n",
    "a - b  # difference\n",
    "\n",
    "b + a  # addition\n",
    "\n",
    "a ** b  # power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 / 5  # int type inferred => floor division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9589242746631385"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math  # importing the library into the namespace\n",
    "\n",
    "math.log(a)  # natural logarithm\n",
    "\n",
    "math.exp(a)  # exponential function\n",
    "\n",
    "math.sin(b)  # sine function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 'Listed Volatility and Variance Derivatives.'\n",
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'LISTED VOLATILITY AND VARIANCE DERIVATIVES.'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.lower()  # converting to lower case characters\n",
    "\n",
    "s.upper()  # converting to upper case characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Listed'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Listed Derivatives\n"
     ]
    }
   ],
   "source": [
    "st = s[0:6] + s[-13:-1]\n",
    "print st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My name is Peter, I am 35 years old and 1.88 m tall.\n"
     ]
    }
   ],
   "source": [
    "repl = \"My name is %s, I am %d years old and %4.2f m tall.\"\n",
    "## replace %s by a string, %d by an integer and\n",
    "## %4.2f by a float showing 2 decimal values\n",
    "print repl % ('Peter', 35, 1.88)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My name is Peter, I am 35 years old and 1.88 m tall.\n"
     ]
    }
   ],
   "source": [
    "repl = \"My name is {:s}, I am {:d} years old and {:4.2f} m tall.\"\n",
    "print repl.format('Peter', 35, 1.88)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = (a, b, st)\n",
    "t1\n",
    "\n",
    "type(t1)\n",
    "\n",
    "t2 = st, b, a\n",
    "t2\n",
    "\n",
    "type(t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Listed Derivatives'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = (t1, t2)\n",
    "\n",
    "t\n",
    "\n",
    "t[0][2]  # take 3rd element of 1st element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 5.0, 'Listed Derivatives', 'Variance']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [a, b, st]\n",
    "l\n",
    "\n",
    "type(l)\n",
    "\n",
    "l.append(s.split()[3])  # append 4th word of string\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 5.0, 'D', 'E', 'H', 'J', 'Q', 'Z']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = list(('Z', 'Q', 'D', 'J', 'E', 'H', 5., a))\n",
    "l\n",
    "\n",
    "l.sort()  # in-place sorting\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'float_obj': 5.0,\n",
       " 'int_obj': 3,\n",
       " 'long_obj': 10000000000L,\n",
       " 'string_obj': 'Listed Derivatives'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'int_obj': a,\n",
    "    'float_obj': b,\n",
    "    'string_obj': st}\n",
    "type(d)\n",
    "\n",
    "d\n",
    "\n",
    "d['float_obj']  # look-up of value given key\n",
    "\n",
    "d['long_obj'] = c / 10 ** 90  # adding new key value pair\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10000000000L, 3, 5.0, 'Listed Derivatives']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.keys()\n",
    "\n",
    "d.values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Control Structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 5, 7, 9, 11, 13]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(5)  # all integers from zero to 5 excluded\n",
    "\n",
    "range(3, 15, 2)  # start at 3, step with 2 until 15 excluded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 4 9 16\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print i ** 2,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 5.0 D E H J Q Z\n"
     ]
    }
   ],
   "source": [
    "## iteration over list object\n",
    "for _ in l:\n",
    "    print _,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L| i| s| t| e| d|  | D| e| r| i| v| a| t| i| v| e| s|\n"
     ]
    }
   ],
   "source": [
    "## iteration over string object\n",
    "for c in st:\n",
    "    print c + '|',"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 1.0 1.41421356237 1.73205080757 2.0\n"
     ]
    }
   ],
   "source": [
    "i = 0  # initialize counter\n",
    "while i < 5:\n",
    "    print i ** 0.5,  # output\n",
    "    i += 1  # increase counter by 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Special Python Idioms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc = [i ** 2 for i in range(10)]\n",
    "lc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(lc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2836621854632263"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = lambda x: math.cos(x) # returns cos of x\n",
    "f(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.5403023058681398,\n",
       " -0.4161468365471424,\n",
       " -0.9899924966004454,\n",
       " -0.6536436208636119,\n",
       " 0.2836621854632263,\n",
       " 0.9601702866503661,\n",
       " 0.7539022543433046,\n",
       " -0.14550003380861354,\n",
       " -0.9111302618846769]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc = [f(x) for x in range(10)]\n",
    "lc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.5403023058681398,\n",
       " -0.4161468365471424,\n",
       " -0.9899924966004454,\n",
       " -0.6536436208636119,\n",
       " 0.2836621854632263,\n",
       " 0.9601702866503661,\n",
       " 0.7539022543433046,\n",
       " -0.14550003380861354,\n",
       " -0.9111302618846769]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map(lambda x: math.cos(x), range(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148.4131591025766"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(x):\n",
    "    return math.exp(x)\n",
    "f(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 5.0, 'D', 'E', 'H', 'J', 'Q', 'Z']\n"
     ]
    }
   ],
   "source": [
    "def f(*args):  # multiple arguments\n",
    "    for arg in args:\n",
    "        print arg\n",
    "        # do something with arguments\n",
    "    return None  # return result(s) (not necessary)\n",
    "\n",
    "f(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random number is 769\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Number is triple digit.'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random  # import random number library\n",
    "a = random.randint(0, 1000)  # draw random number between 0 and 1000\n",
    "print \"Random number is %d\" % a\n",
    "def number_decide(number):\n",
    "    if a < 10:\n",
    "        return \"Number is single digit.\"\n",
    "    elif 10 <= a < 100:\n",
    "        return \"Number is double digit.\"\n",
    "    else:\n",
    "        return \"Number is triple digit.\"\n",
    "number_decide(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 9\n"
     ]
    }
   ],
   "source": [
    "g = xrange(10)\n",
    "type(g)  # object type\n",
    "\n",
    "g  # object instance\n",
    "\n",
    "for _ in g:\n",
    "    print _,  # integers are \"generated\" when needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 97.8 ms, sys: 84.9 ms, total: 183 ms\n",
      "Wall time: 183 ms\n"
     ]
    }
   ],
   "source": [
    "%time r = range(10000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80000072"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.getsizeof(r)  # size in bytes of object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs\n",
      "Wall time: 6.2 µs\n"
     ]
    }
   ],
   "source": [
    "%time xr = xrange(10000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sys.getsizeof(xr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 147 ms, sys: 12.1 ms, total: 159 ms\n",
      "Wall time: 156 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "499999500000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time reduce(lambda x, y: x + y, range(1000000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 142 ms, sys: 6.05 ms, total: 148 ms\n",
      "Wall time: 143 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "499999500000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time reduce(lambda x, y: x + y, xrange(1000000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 15.7 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit sum(range(1000000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 7.99 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit sum(xrange(1000000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object <genexpr> at 0x1061d35a0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = (math.sin(x) for x in xrange(100))\n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8414709848078965"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.next()\n",
    "g.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def g(start, end):\n",
    "    while start <= end:\n",
    "        yield start  # yield \"next\" value\n",
    "        start += 1  # increase by one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 16 17 18 19 20\n"
     ]
    }
   ],
   "source": [
    "go = g(15, 20)\n",
    "for _ in go:\n",
    "    print _,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array(range(24))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a.reshape((4, 6))\n",
    "b\n",
    "\n",
    "c = a.reshape((2, 3, 4))\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   1.,   2.,   3.,   4.,   5.],\n",
       "       [  6.,   7.,   8.,   9.,  10.,  11.],\n",
       "       [ 12.,  13.,  14.,  15.,  16.,  17.],\n",
       "       [ 18.,  19.,  20.,  21.,  22.,  23.]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array(b, dtype=np.float)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0.,    1.,    4.,    9.,   16.,   25.],\n",
       "       [  36.,   49.,   64.,   81.,  100.,  121.],\n",
       "       [ 144.,  169.,  196.,  225.,  256.,  289.],\n",
       "       [ 324.,  361.,  400.,  441.,  484.,  529.]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 * b\n",
    "\n",
    "b ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0.5,   -0.5,    0.5,    3.5,    8.5,   15.5,   24.5,   35.5,\n",
       "         48.5,   63.5,   80.5,   99.5,  120.5,  143.5,  168.5,  195.5,\n",
       "        224.5,  255.5,  288.5,  323.5,  360.5,  399.5,  440.5,  483.5])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = lambda x: x ** 2 - 2 * x + 0.5\n",
    "f(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  8.,   9.],\n",
       "       [ 14.,  15.]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:6]  # 3rd to 6th element\n",
    "\n",
    "b[2, 4]  # 3rd row, final (5th)\n",
    "\n",
    "b[1:3, 2:4]  # middle square of numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,\n",
       "        22.,  23.])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## which numbers are larger than 10?\n",
    "b > 10\n",
    "\n",
    "## only those numbers (flat) that are larger than 10\n",
    "b[b > 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.9221865524317288"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum()  # sum of all elements\n",
    "\n",
    "b.mean()  # mean of all elements\n",
    "\n",
    "b.mean(axis=0)  # mean along 1st axis\n",
    "\n",
    "b.mean(axis=1)  # mean along 2nd axis\n",
    "\n",
    "c.std()  # standard deviation for all elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.97753011766509701"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(a)  # sum of all elements\n",
    "\n",
    "np.mean(b, axis=0)  # mean alond 1st axis\n",
    "\n",
    "np.sin(b).round(2)  # sine of all elements (rounded)\n",
    "\n",
    "np.sin(4.5)  # sine of Python float object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 120 ms, sys: 11.1 ms, total: 131 ms\n",
      "Wall time: 125 ms\n"
     ]
    }
   ],
   "source": [
    "%time l = [np.sin(x) for x in xrange(100000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 29.8 ms, sys: 4.98 ms, total: 34.8 ms\n",
      "Wall time: 32.4 ms\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "%time l = [math.sin(x) for x in xrange(100000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.23 ms, sys: 1.17 ms, total: 3.41 ms\n",
      "Wall time: 2.32 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.84147098,  0.90929743, ...,  0.10563876,\n",
       "        0.89383946,  0.86024828])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time np.sin(np.arange(100000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ai = np.arange(10)\n",
    "ai\n",
    "\n",
    "ai.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "af = np.arange(0.5, 9.5, 0.5)  # start, end, step size\n",
    "af\n",
    "\n",
    "af.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        ,   0.90909091,   1.81818182,   2.72727273,\n",
       "         3.63636364,   4.54545455,   5.45454545,   6.36363636,\n",
       "         7.27272727,   8.18181818,   9.09090909,  10.        ])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 10, 12)  # start, end, number of elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 1, 0, 1, 0, 0])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.standard_normal(10) \n",
    "\n",
    "np.random.poisson(0.5, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(1000)  # fix the rng seed value\n",
    "data = np.random.standard_normal((5, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0016799134894265"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean()  # should be 0.0\n",
    "\n",
    "data.std()  # should be 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data - data.mean()  # correction for the 1st moment\n",
    "data = data / data.std()  # correction for the 2nd moment\n",
    "\n",
    "data.mean()  # now really close to 0.0\n",
    "\n",
    "data.std()  # now really close to 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt  # import main plotting library\n",
    "import seaborn as sns; sns.set()  # set seaborn standards\n",
    "import matplotlib\n",
    "## set font to serif\n",
    "matplotlib.rcParams['font.family'] = 'serif'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NxBRX56VslbJe1e4RO37tJ6RC7i89fAEPPyv1mlKyMqWF9eFYBtF4BlqNAJ22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\nEogJIYQQQmyQ/w/BFspXMfP0vwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e98e510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6));  # size of figure\n",
    "plt.plot(data.cumsum());  # cumulative sum over all elements "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pz1jPBkAhsPhLKEqlMLfBYMbXcjVO3xmEu58mFf4Ym6sNp39VbQNegCSJE0JM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\nFBTQePAAtdu3UfXhOor/9heacg/jP3MWAT3clWIwtLpa+Y8ug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JH9imlv6X6ey3oFP4OZ\n+1LuOmve3ltH1vF5/hYmhqp8a+odvJT9OqklaXxv+j2MCx59+ri+tl9JYynPZb1CQcNJpodP4WuT\nbjxvD+Vbuev4vGALa5Iu5tKklec87kjNcf687+/46c1clrSKi2Lnd5nsHK4+wss5b1LeVMnEUJV7\np93Z69fRF025ubgaGzCGh2MMC0dnNnc65lClxvOH/ktDWyN2kz9NbU04Pa6zjlkRdxFXj+tuA6ru\nhYfb+z35T5I4MSxcKG8kI5W0n++60Npuc+EO3sp9H6fHhUFnYHr4FBbGzGFs0Ghe0d5i+8lU5kTN\n4LYJ13c5f25L4U5ePfw2Bp0Bm8GKSW/EpDdh0pkw600sjJ3bo3ldaaUZPJv1Mma9me9Ov6vTwgu3\nx83TGc9yqErjotgF7CreQ4DJzv+b/+OzVq/2p/2cbif59YUkBsR3uyL2lCZnM7/Y9XuanM38fO4D\nhFo6r9hscjbz691/pKq5BrPeTLOrmShrBNeMu5yJoSoAjrYm3j7yATuKd6OgsCxuEWtGr8Y8iL1w\nfVHTUsvLOW9S1FBMoCmAALOdQJOdAHMAO07uxtHm4LEFP8PfZOvT9SWJO5skcT7sQnsjGWmk/XzX\nhdh29a0NpJaksf1kKmWOCgCCzUFUt9QQ6x/NAzPv7XZYb8fJ3Wwq3E6rq7X9y91Gq6sNl8eFTtHx\n3ZS7z+ot+7IjNcf5v33/wKgz8p2Ub5AU2HklKrQnO/+b9hSljnIALktaxVeSVp11zFC3X2pxGi9k\nv8r08Cl8Y8qtnZ5/4dCrpJakcUniCpaOWsi6Y5+w/eRuPHiYHJrM1PBJrDu2nrrWemL9o7k5+dpe\nrxweDk6VYrkkYTmXj+n1DqOAJHFfJkmcD7sQ30hGEmk/33Uht53H4+FIzXG2n9zNvvJMzHoTP551\nX58n7udWH+P/9v8Dm8HKT2Z/l2C/oE7HVDRV8fu9T+JwNvGdad9ADRnb7TVLHeX8fu+TNDmbeXT+\ng4R9qWbbULef2+PmD2lPc7wuj/tS7jqriHB6WSb/OvgS8fZRPDDz3tNDqIX1J3kj9z1ya44BYNAZ\nuDRxJavil/jc1mCntLpaeXjHb2hzO3lswU+7XbV7rtp/A5HEycIGMSyM9MnVI520n++6kNtOURRC\nLcGkRExm6agFLBm1sF+14EItwViNFvaVH+BozQnmRM04K0lpcjbz5P5/UNVczfXqWmZETjvvNf2N\nNiaEqEwMVRkdmNDp+aFuv1OrVnec3E1efSGLYuaiU3RUN9fwdMa/UVD4zvS7zlr4EWC2MzdqJrH+\n0fjp/fjaxOtJiZjcoyHc4Uqv0+PGTVZlDn4GM2ODuu55LWks5Ve7/4Re0XfqcR2IhQ2++x0UQggh\nBojFYOm0MrMvlsQuYF7ULPLqC/iv9vbpvUvdHjfPZb1McWMpS0YtZHHs/B5fM84ew7TwSf2ObaDE\n20exIGYOJY2lbC7agdvj5sXs13A4m7h63OVEWsM7naMoCikRU7h5wrVE2iK8EPXAWxw7H6vBwoaC\nrbS4OifSzc4WnjnwInWt9Z16UAeKJHFCCCHEAFEUhRvUq4i3j2JXyV42F+0A4J0jH3KwMocJIeO5\nZmzfVzQOF1eMvgSrwcIHxz7l/WOfoFUfYXLoBBbFzPV2aEPGYvBjadwiGtscbCvaddZzHo+HV7Q3\nKXGUsWzUIqaETRyUGCSJE0IIIQaQUW/k7im3YTf682bu+7x2+F0+L9hCpDWcr0+62WfngZ3J32Rj\nzejVNLuaWZ+3EbvRn1smXDcsN4kfTEtHLcSsN/F5/mbaXG2nH99ctIO9pftJCkhg7djLBu3+ksQJ\nIYQQAyzYL4g7J98CwObC7VgNFu6ZevugbFvlLYti5p7eYeHmCdcOWAFkX2IzWrkodgG1rfXsLN4L\nwPHaPN7KXYe/0cadk2/GcJ76e/0hSZwQQggxCMYFj+YG9SqCzUHcNeVWIrqYK+bL9Do93025mx/O\n/PagDRf6guXxizHqDHyav4naljr+dfA/uD1u7ph0U5crlAfS4KWHQgghxAVuYcxcFkTPGbHDjP4m\nW5+L3Y4UASY7C2PmsqlwO7/b+yQ1LbVcPnr1WeVXBov0xAkhhBCDaKQmcOILK+OXoFf01LTUMjk0\nmYsTlg3JfSWJE0IIIYToh2C/IC5OWEpCQBy3TbxhyGrgyXCqEEIIIUQ/rRm9mjWjVw/pPaUnTggh\nhBDCB0kSJ4QQQgjhgySJE0IIIYTwQZLECSGEEEL4IEnihBBCCCF8kCRxQgghhBA+SJI4IYQQQggf\nJEmcEEIIIYQPkiROCCGEEMIHSRInhBBCCOGDJIkTQgghhPBBksQJIYQQQvggSeKEEEIIIXyQJHFC\nCCGEED5IkjghhBBCCB9k8NaNVVX9A9DY8TUNuF/TtLKO5x4AAoAg4FNN0973VpxCCCGEEMORN3vi\nGjRN+7mmab8B9gEPAaiqOgdYqmnaw8D3gSdUVbV7MU4hhBBCiGHHa0lcR5J2ZhwNHf+/BtjZcYwL\nyAaWDG10QgghhBDD26AOp6qq+jEQccZDCuABHtY0bV3HMUHAKuDqjmMigENnnFP3pWsIIYQQQlzw\nBjWJ0zTtku6eV1U1AHgSuEPTtNqOh8uAM4dPAzoeOx8lPFxGXX2ZtJ9vk/bzXdJ2vk3a78LlteFU\nVVXDgL8AP9Y0LV9V1VM9cR8A8zuOMQLJwBbvRCmEEEIIMTwpHo/HKzdWVTUN0ANVtA+z1mmadmXH\ncz8EQmhfnfrRqaFXIYQQQgjRzmtJnBBCCCGE6Dsp9iuEEEII4YMkiRNCCCGE8EGSxAkhhBBC+CCv\nbbs1kFRVXUF7nblSAE3TfuHdiMS5qKo6GngcSAPigEpN0x5TVTUY+A1wDBgL/EzTtHLvRSrORVVV\nPyAV+ETTtB9L2/kOVVXHAzcCTcBFwCPAUaT9fELHlpQJQCUwDvg6YEXab1hSVTWS9ve7aZqmzel4\n7Jx/L/uy5ajP98SpqmoB/kb73qu/AKaqqrrMy2GJcwsBXtE07QlN074H3KCq6nTgV7T/0P4WeBd4\nwptBim49DqSf8W9pOx+gqqoO+IOmaY9qmvY74E7gBNJ+PqEjIfippmn3aZr2CGADrkHabzhbSHub\nnKnL9urrlqM+n8TRXlPuhKZpzo5/bwe+4sV4RDc0Tdv7pU8XCtBIe5vt7HhM2nCYUlX1FmAb7W/+\np0jb+YbZgKKq6n2qqj4IXK5pWgXSfr7CAbR0FMmH9iTuINJ+w5amaW8B9V96+MvtdVnH//dpy9GR\nkMRFcPY3Sbbp8hGqqq6lfUjuMGe3Yx0Q1NFzIIYJVVUnAMmapr3zpaek7XxDAjAPeFbTtN8AS1RV\n/RoQjrTfsKdpWj3wY+A1VVWfBQppHwqX3z/f8uX2Cu5orz7lMiOhoctoH0M+pafbdAkvUlV1Ke1d\nx9/veKiUL7ZbCwCqNU1zeyM2cU5XAc2qqv4EWATMUVX1fqTtfEUdkKNpWkPHv7cBSzl7q0Npv2FK\nVdVpwI+ASzVNu4P2eXEPI79/vuZc7dWnLUdHQhK3E4jv2KIL2segP/BiPOI8VFX9CrBa07Tvqaoa\nrarqPM7Ybg1pw2FJ07RfaZr2eMdcjm3Abk3T/oy0na9IBUJVVVU6/p0AaEj7+YpY2heCnarQXwyY\nkfbzBcoZ/39mey3ii/bq05ajI2LHho7VqdfRnrW2aZr2mJdDEuegquoMYDOwh/YfbCvte+i+B/wW\nyAdGAw/KCqvhqWOf428DJtrb7hOk7XyCqqpXAiuActpXh9/HF6sbpf2GsY4htz8DzUAtMAn4HtCK\ntN+wpKrqRcBtwGrgadoXMZzz960vW46OiCROCCGEEOJCMxKGU4UQQgghLjiSxAkhhBBC+CBJ4oQQ\nQgghfJAkcUIIIYQQPkiSOCGEEEIIHyRJnBBCCCGED5IkTgjhs1RVjVVV9V1VVTf04NglqqpuHIq4\nOu4XqqpqTg+PfVZV1dsGOyYhxMgiSZwQwmdpmlYE/KEXpwxZYUxN0yqBi4fqfkKIC48U+xVC+BxV\nVe8DrgZyaN80epamactVVdUDb3c87gdUaZr2iKqqwcC/gNkdz+8AXu/q2C7udS3wC2AfUAOMBdI0\nTftZx/PLgbuBPNp3QfiZpmknOvaY/RkwhfYtk56hfZurOmAG8J6maT9XVfVS2iu5F3bE8gdN004M\n3HdLCDFSSRInhPApqqpOAdYDCZqmtaqq+hiw8Iwk7gpN097uOHYd8KimaXv+f3t3DBpFFARg+I8K\n9jFYpFDTOCB2KiokGFMo9iJaSGIpgn1QEExlFbAWAlZiY20hBlQEJYUIwggaiCBanCCIRNFg8V7g\njHcXxYjZ8H/VPd7s270tjrk3O2xEHAGuZOZYnesa2+GcM0BfZk7U1x/NAZco7yN9AezJzFZEHAOm\nMvNgPe41MJqZCxExDkwCeylJ4ztgIDMX6/r3M/Pmv7lrkjYiy6mSmmYUeJqZX+v4wfJEZn4HdkbE\njYiYBnYBuzst8iex1aN63BLwEBijvLD6fS2dLscciIj+Ou5bscZcZn7LzE9ACxhY/etKUmdb/vcF\nSNJaiYgzwNnM3FfHM8DmLrGnfze2h06ljF7ljS9tn5fwj7Skv+APiKSmmQX2R8TWOh5um+sHPraN\nd7R9XqQmaRExAWzrEdvJ4XrsJmAEuAc8BrZHxPKO2jBll/BDlzVW7sz9dG0RMRQRI6tchyQBPhMn\nqYEi4gJwEnhOeb7sBDAF3AJuU5KzBUrJswWcB94Cd4GXwIE8zLgAAACXSURBVDxwndLc8EtsZr5a\ncb4Z4DOlejEEPMnMy3XuaF3/DTBIaWyYr40Nk5TmiWlK88IgcJHSHHENuJOZ5yLieI1tAVcz89ka\n3i5JG5RJnCStwsYDSeuR5VRJ6iEiTgGHgPHaGStJ64I7cZIkSQ3kTpwkSVIDmcRJkiQ1kEmcJElS\nA5nESZIkNZBJnCRJUgOZxEmSJDXQD/tbiJwKCdErAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130fe8090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6));  # size of figure\n",
    "## plotting five cumulative sums as lines\n",
    "plt.plot(data.T.cumsum(axis=0), label='line');\n",
    "plt.legend(loc=0);  # legend in best location\n",
    "plt.xlabel('data point');  # x axis label\n",
    "plt.ylabel('value');  # y axis label\n",
    "plt.title('random series');  # figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PgjcTU0du+PXTSe5L8mQ2j6l6etxFlpdv/sSQ3bSwcHRq9nxl5dp+jwAcMrPzi5k7dry8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13118e810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6));  # size of figure\n",
    "plt.hist(data.flatten(), bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MYztZlDm3n3Mx5r2/LMq+skjM+eK71CA77WL9O5N8TVXdmeQbkrxlUn5Nkl9Mkqq6Ocm9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13139a690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6));  # size of figure\n",
    "plt.bar(np.arange(1, 12) - 0.25, data[0, :11], width=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.arange(len(data.cumsum()))\n",
    "y = data.cumsum()\n",
    "rg1 = np.polyfit(x, y, 1)  # linear OLS\n",
    "rg2 = np.polyfit(x, y, 2)  # quadratic OLS\n",
    "rg3 = np.polyfit(x, y, 3)  # cubic OLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EgZgxpBAzPQPH8mK0ks3WthSWwTgc2B0EIiNPkbZT7aFt3mx/7frk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1AAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1315da110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6));\n",
    "plt.plot(x, y, 'r', label='data');\n",
    "plt.plot(x, np.polyval(rg1, x), 'b--', label='linear');\n",
    "plt.plot(x, np.polyval(rg2, x), 'b-.', label='quadratic');\n",
    "plt.plot(x, np.polyval(rg3, x), 'b:', label='cubic');\n",
    "plt.legend(loc=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pandas DataFrame class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(1000)\n",
    "a = np.random.standard_normal((10, 3)).cumsum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index = ['2016-1-31', '2016-2-28', '2016-3-31',\n",
    "        '2016-4-30', '2016-5-31', '2016-6-30',\n",
    "        '2016-7-31', '2016-8-31', '2016-9-30',\n",
    "        '2016-10-31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns = ['no1', 'no2', 'no3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(a, index=index, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>no1</th>\n",
       "      <th>no2</th>\n",
       "      <th>no3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-1-31</th>\n",
       "      <td>-0.804458</td>\n",
       "      <td>0.320932</td>\n",
       "      <td>-0.025483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-2-28</th>\n",
       "      <td>-0.160134</td>\n",
       "      <td>0.020135</td>\n",
       "      <td>0.363992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-3-31</th>\n",
       "      <td>-0.267572</td>\n",
       "      <td>-0.459848</td>\n",
       "      <td>0.959027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-4-30</th>\n",
       "      <td>-0.732239</td>\n",
       "      <td>0.207433</td>\n",
       "      <td>0.152912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-5-31</th>\n",
       "      <td>-1.928309</td>\n",
       "      <td>-0.198527</td>\n",
       "      <td>-0.029466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-6-30</th>\n",
       "      <td>-1.825116</td>\n",
       "      <td>-0.336949</td>\n",
       "      <td>0.676227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-7-31</th>\n",
       "      <td>-0.553321</td>\n",
       "      <td>-1.323696</td>\n",
       "      <td>0.341391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-8-31</th>\n",
       "      <td>-0.652803</td>\n",
       "      <td>-0.916504</td>\n",
       "      <td>1.260779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-9-30</th>\n",
       "      <td>-0.340685</td>\n",
       "      <td>0.616657</td>\n",
       "      <td>0.710605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-31</th>\n",
       "      <td>-0.723832</td>\n",
       "      <td>-0.206284</td>\n",
       "      <td>2.310688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 no1       no2       no3\n",
       "2016-1-31  -0.804458  0.320932 -0.025483\n",
       "2016-2-28  -0.160134  0.020135  0.363992\n",
       "2016-3-31  -0.267572 -0.459848  0.959027\n",
       "2016-4-30  -0.732239  0.207433  0.152912\n",
       "2016-5-31  -1.928309 -0.198527 -0.029466\n",
       "2016-6-30  -1.825116 -0.336949  0.676227\n",
       "2016-7-31  -0.553321 -1.323696  0.341391\n",
       "2016-8-31  -0.652803 -0.916504  1.260779\n",
       "2016-9-30  -0.340685  0.616657  0.710605\n",
       "2016-10-31 -0.723832 -0.206284  2.310688"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>no1</th>\n",
       "      <th>no2</th>\n",
       "      <th>no3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-1-31</th>\n",
       "      <td>-0.804458</td>\n",
       "      <td>0.320932</td>\n",
       "      <td>-0.025483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-2-28</th>\n",
       "      <td>-0.160134</td>\n",
       "      <td>0.020135</td>\n",
       "      <td>0.363992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-3-31</th>\n",
       "      <td>-0.267572</td>\n",
       "      <td>-0.459848</td>\n",
       "      <td>0.959027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-4-30</th>\n",
       "      <td>-0.732239</td>\n",
       "      <td>0.207433</td>\n",
       "      <td>0.152912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-5-31</th>\n",
       "      <td>-1.928309</td>\n",
       "      <td>-0.198527</td>\n",
       "      <td>-0.029466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                no1       no2       no3\n",
       "2016-1-31 -0.804458  0.320932 -0.025483\n",
       "2016-2-28 -0.160134  0.020135  0.363992\n",
       "2016-3-31 -0.267572 -0.459848  0.959027\n",
       "2016-4-30 -0.732239  0.207433  0.152912\n",
       "2016-5-31 -1.928309 -0.198527 -0.029466"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()  # first five rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>no1</th>\n",
       "      <th>no2</th>\n",
       "      <th>no3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-6-30</th>\n",
       "      <td>-1.825116</td>\n",
       "      <td>-0.336949</td>\n",
       "      <td>0.676227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-7-31</th>\n",
       "      <td>-0.553321</td>\n",
       "      <td>-1.323696</td>\n",
       "      <td>0.341391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-8-31</th>\n",
       "      <td>-0.652803</td>\n",
       "      <td>-0.916504</td>\n",
       "      <td>1.260779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-9-30</th>\n",
       "      <td>-0.340685</td>\n",
       "      <td>0.616657</td>\n",
       "      <td>0.710605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-31</th>\n",
       "      <td>-0.723832</td>\n",
       "      <td>-0.206284</td>\n",
       "      <td>2.310688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 no1       no2       no3\n",
       "2016-6-30  -1.825116 -0.336949  0.676227\n",
       "2016-7-31  -0.553321 -1.323696  0.341391\n",
       "2016-8-31  -0.652803 -0.916504  1.260779\n",
       "2016-9-30  -0.340685  0.616657  0.710605\n",
       "2016-10-31 -0.723832 -0.206284  2.310688"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()  # last five rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'2016-1-31', u'2016-2-28', u'2016-3-31', u'2016-4-30', u'2016-5-31',\n",
       "       u'2016-6-30', u'2016-7-31', u'2016-8-31', u'2016-9-30', u'2016-10-31'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index  # index object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'no1', u'no2', u'no3'], dtype='object')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns  # column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 10 entries, 2016-1-31 to 2016-10-31\n",
      "Data columns (total 3 columns):\n",
      "no1    10 non-null float64\n",
      "no2    10 non-null float64\n",
      "no3    10 non-null float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 320.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()  # meta information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>no1</th>\n",
       "      <th>no2</th>\n",
       "      <th>no3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.798847</td>\n",
       "      <td>-0.227665</td>\n",
       "      <td>0.672067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.607430</td>\n",
       "      <td>0.578071</td>\n",
       "      <td>0.712430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.928309</td>\n",
       "      <td>-1.323696</td>\n",
       "      <td>-0.029466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.786404</td>\n",
       "      <td>-0.429123</td>\n",
       "      <td>0.200031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.688317</td>\n",
       "      <td>-0.202406</td>\n",
       "      <td>0.520109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.393844</td>\n",
       "      <td>0.160609</td>\n",
       "      <td>0.896922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-0.160134</td>\n",
       "      <td>0.616657</td>\n",
       "      <td>2.310688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             no1        no2        no3\n",
       "count  10.000000  10.000000  10.000000\n",
       "mean   -0.798847  -0.227665   0.672067\n",
       "std     0.607430   0.578071   0.712430\n",
       "min    -1.928309  -1.323696  -0.029466\n",
       "25%    -0.786404  -0.429123   0.200031\n",
       "50%    -0.688317  -0.202406   0.520109\n",
       "75%    -0.393844   0.160609   0.896922\n",
       "max    -0.160134   0.616657   2.310688"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()  # typical statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no1   -0.798847\n",
       "no2   -0.227665\n",
       "no3    0.672067\n",
       "dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df * 2  # vectorized multiplication\n",
    "\n",
    "df.std()  # standard deviation by column\n",
    "\n",
    "df.mean(axis=1)  # mean by index value\n",
    "\n",
    "np.mean(df)  # mean via universal function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-1-31     0.320932\n",
       "2016-2-28     0.020135\n",
       "2016-3-31    -0.459848\n",
       "2016-4-30     0.207433\n",
       "2016-5-31    -0.198527\n",
       "2016-6-30    -0.336949\n",
       "2016-7-31    -1.323696\n",
       "2016-8-31    -0.916504\n",
       "2016-9-30     0.616657\n",
       "2016-10-31   -0.206284\n",
       "Name: no2, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['no2']  # 2nd column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no1   -0.804458\n",
       "no2    0.320932\n",
       "no3   -0.025483\n",
       "Name: 2016-1-31, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]  # 1st row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>no1</th>\n",
       "      <th>no2</th>\n",
       "      <th>no3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-3-31</th>\n",
       "      <td>-0.267572</td>\n",
       "      <td>-0.459848</td>\n",
       "      <td>0.959027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-4-30</th>\n",
       "      <td>-0.732239</td>\n",
       "      <td>0.207433</td>\n",
       "      <td>0.152912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                no1       no2       no3\n",
       "2016-3-31 -0.267572 -0.459848  0.959027\n",
       "2016-4-30 -0.732239  0.207433  0.152912"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:4]  # 3rd & 4th row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-3-31   -0.459848\n",
       "2016-4-30    0.207433\n",
       "Name: no2, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:4, 1]  # 3rd & 4th row, 2nd column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-4-30    0.152912\n",
       "2016-5-31   -0.029466\n",
       "2016-6-30    0.676227\n",
       "2016-7-31    0.341391\n",
       "Name: no3, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.no3.iloc[3:7]  # dot look-up for column name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no1   -0.267572\n",
       "no2   -0.459848\n",
       "no3    0.959027\n",
       "Name: 2016-3-31, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2016-3-31']  # row given index value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.029465774923291221"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2016-5-31', 'no3']  # single data point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-1-31    -0.880907\n",
       "2016-2-28     0.931841\n",
       "2016-3-31     2.609510\n",
       "2016-4-30    -0.273505\n",
       "2016-5-31    -2.016706\n",
       "2016-6-30     0.203564\n",
       "2016-7-31     0.470852\n",
       "2016-8-31     3.129533\n",
       "2016-9-30     1.791130\n",
       "2016-10-31    6.208233\n",
       "dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['no1'] + 3 * df['no3']  # vectorized arithmetic operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>no1</th>\n",
       "      <th>no2</th>\n",
       "      <th>no3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-5-31</th>\n",
       "      <td>-1.928309</td>\n",
       "      <td>-0.198527</td>\n",
       "      <td>-0.029466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-6-30</th>\n",
       "      <td>-1.825116</td>\n",
       "      <td>-0.336949</td>\n",
       "      <td>0.676227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-7-31</th>\n",
       "      <td>-0.553321</td>\n",
       "      <td>-1.323696</td>\n",
       "      <td>0.341391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-8-31</th>\n",
       "      <td>-0.652803</td>\n",
       "      <td>-0.916504</td>\n",
       "      <td>1.260779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-9-30</th>\n",
       "      <td>-0.340685</td>\n",
       "      <td>0.616657</td>\n",
       "      <td>0.710605</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                no1       no2       no3\n",
       "2016-5-31 -1.928309 -0.198527 -0.029466\n",
       "2016-6-30 -1.825116 -0.336949  0.676227\n",
       "2016-7-31 -0.553321 -1.323696  0.341391\n",
       "2016-8-31 -0.652803 -0.916504  1.260779\n",
       "2016-9-30 -0.340685  0.616657  0.710605"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['no3'] > 0.5\n",
    "\n",
    "df[df['no3'] > 0.5]\n",
    "\n",
    "df[(df.no3 > 0.5) & (df.no2 > -0.25)]\n",
    "\n",
    "df[df.index > '2016-4-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2En3V1ZRWO6Da/f/untbMu3oWre5//2OxWCIsFov5+P//22KxGI7/qB1w6GzNnRBCCCHE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85UqzwK/MTdPzSzHwB3AffUXxl4190fCONN/M4eBbwPlDcjb6CVBZ67L6r3VCxsdBJBtQzwDjAz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e8c9a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OYAVwU2aOVVemJBXnWCepiHau2VoDjDdMP1Of1+h+YFNmbgYeBm6rpDpJ6hzHOklFtHNm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\nSSro/wN5iwWg46IepQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131922350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input-Output Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('data.csv')  # exports to CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",no1,no2,no3\n",
      "2016-1-31,-0.804458303525,0.32093154709,-0.0254828804721\n",
      "2016-2-28,-0.160134475098,0.0201348743028,0.363991673815\n",
      "2016-3-31,-0.267571776789,-0.459848201058,0.959027175892\n",
      "2016-4-30,-0.732239302984,0.20743310593,0.152911565449\n",
      "2016-5-31,-1.92830913682,-0.19852705543,-0.0294657749233\n",
      "2016-6-30,-1.82511624278,-0.336949044016,0.676226600036\n",
      "2016-7-31,-0.553320966375,-1.32369637281,0.341391146824\n",
      "2016-8-31,-0.652802664384,-0.916504272472,1.26077868603\n",
      "2016-9-30,-0.340684654318,0.616656792886,0.710604821\n",
      "2016-10-31,-0.723832065202,-0.206284170553,2.31068818906\n"
     ]
    }
   ],
   "source": [
    "with open('data.csv') as f:  # open file\n",
    "    for l in f.readlines():  # iterate over all lines\n",
    "        print l,  # print line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>no1</th>\n",
       "      <th>no2</th>\n",
       "      <th>no3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>-0.804458</td>\n",
       "      <td>0.320932</td>\n",
       "      <td>-0.025483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28</th>\n",
       "      <td>-0.160134</td>\n",
       "      <td>0.020135</td>\n",
       "      <td>0.363992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>-0.267572</td>\n",
       "      <td>-0.459848</td>\n",
       "      <td>0.959027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-30</th>\n",
       "      <td>-0.732239</td>\n",
       "      <td>0.207433</td>\n",
       "      <td>0.152912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-31</th>\n",
       "      <td>-1.928309</td>\n",
       "      <td>-0.198527</td>\n",
       "      <td>-0.029466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 no1       no2       no3\n",
       "2016-01-31 -0.804458  0.320932 -0.025483\n",
       "2016-02-28 -0.160134  0.020135  0.363992\n",
       "2016-03-31 -0.267572 -0.459848  0.959027\n",
       "2016-04-30 -0.732239  0.207433  0.152912\n",
       "2016-05-31 -1.928309 -0.198527 -0.029466"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from_csv = pd.read_csv('data.csv',  # filename\n",
    "                      index_col=0,  # index column\n",
    "                      parse_dates=True)  # date index\n",
    "from_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.io.pytables.HDFStore'>\n",
       "File path: data.h5\n",
       "/df            frame        (shape->[10,3])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5 = pd.HDFStore('data.h5', 'w')  # open for writing\n",
    "h5['df'] = df  # write object to database\n",
    "h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from_h5 = h5['df']  # reading from database\n",
    "h5.close()  # closing the database\n",
    "from_h5.tail()\n",
    "!rm data.csv data.h5 # remove the objects from disk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Financial Analytics Examples"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pandas_datareader import data as web\n",
    "spx = web.DataReader('^GSPC', data_source='yahoo', end='2015-12-31')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1510 entries, 2010-01-04 to 2015-12-31\n",
      "Data columns (total 6 columns):\n",
      "Open         1510 non-null float64\n",
      "High         1510 non-null float64\n",
      "Low          1510 non-null float64\n",
      "Close        1510 non-null float64\n",
      "Volume       1510 non-null int64\n",
      "Adj Close    1510 non-null float64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 82.6 KB\n"
     ]
    }
   ],
   "source": [
    "spx.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vix = web.DataReader('^VIX', data_source='yahoo', end='2015-12-31')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1510 entries, 2010-01-04 to 2015-12-31\n",
      "Data columns (total 6 columns):\n",
      "Open         1510 non-null float64\n",
      "High         1510 non-null float64\n",
      "Low          1510 non-null float64\n",
      "Close        1510 non-null float64\n",
      "Volume       1510 non-null int64\n",
      "Adj Close    1510 non-null float64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 82.6 KB\n"
     ]
    }
   ],
   "source": [
    "vix.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1510 entries, 2010-01-04 to 2015-12-31\n",
      "Data columns (total 2 columns):\n",
      "CloseSPX    1510 non-null float64\n",
      "CloseVIX    1510 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 35.4 KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1510 entries, 2010-01-04 to 2015-12-31\n",
      "Data columns (total 2 columns):\n",
      "CloseSPX    1510 non-null float64\n",
      "CloseVIX    1510 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 35.4 KB\n"
     ]
    }
   ],
   "source": [
    "## construction via join\n",
    "spxvix = pd.DataFrame(spx['Close']).join(vix['Close'],\n",
    "                        lsuffix='SPX', rsuffix='VIX')\n",
    "spxvix.info()\n",
    "\n",
    "## construction via merge\n",
    "spxvix = pd.merge(pd.DataFrame(spx['Close']),\n",
    "                  pd.DataFrame(vix['Close']),\n",
    "                  left_index=True,  # merge on left index\n",
    "                  right_index=True,  # merge on right index\n",
    "                  suffixes=['SPX', 'VIX'])\n",
    "spxvix.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1510 entries, 2010-01-04 to 2015-12-31\n",
      "Data columns (total 2 columns):\n",
      "SPX    1510 non-null float64\n",
      "VIX    1510 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 35.4 KB\n"
     ]
    }
   ],
   "source": [
    "## construction via dictionary object\n",
    "spxvix = pd.DataFrame({'SPX': spx['Close'],\n",
    "                       'VIX': vix['Close']},\n",
    "                       index=spx.index)\n",
    "spxvix.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kfftGBN1Wv34Uc+Y48r3fTz9ZskNVixbmqsD77w+jSZMoevcOhK1rr5VNoIUo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\ng8I5ciQZh6Pg505Lg4ULHQw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tzJgRwtKldm6/3QzjzZsX731Qs6bBvn1m+YcmTSrPe0cI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\nwWVmZmaWMxdcZmZmZjn7H44wDk/Hazc/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x132064dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spxvix.plot(figsize=(10, 6), subplots=True, color='b');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>VIX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.003111</td>\n",
       "      <td>-0.035038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.000545</td>\n",
       "      <td>-0.009868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.003993</td>\n",
       "      <td>-0.005233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.002878</td>\n",
       "      <td>-0.050024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SPX       VIX\n",
       "Date                          \n",
       "2010-01-04       NaN       NaN\n",
       "2010-01-05  0.003111 -0.035038\n",
       "2010-01-06  0.000545 -0.009868\n",
       "2010-01-07  0.003993 -0.005233\n",
       "2010-01-08  0.002878 -0.050024"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets = np.log(spxvix / spxvix.shift(1))\n",
    "rets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>VIX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.003111</td>\n",
       "      <td>-0.035038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.000545</td>\n",
       "      <td>-0.009868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.003993</td>\n",
       "      <td>-0.005233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.002878</td>\n",
       "      <td>-0.050024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>0.001745</td>\n",
       "      <td>-0.032514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SPX       VIX\n",
       "Date                          \n",
       "2010-01-05  0.003111 -0.035038\n",
       "2010-01-06  0.000545 -0.009868\n",
       "2010-01-07  0.003993 -0.005233\n",
       "2010-01-08  0.002878 -0.050024\n",
       "2010-01-11  0.001745 -0.032514"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets = rets.dropna()\n",
    "rets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AttodgdngkypZIYYoCeqEED2is+03Ott4t/kSbUF+LvMWLqOgqJjE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\n5qdl+Rha+dwo4K6PHuMyg5nlv/gTua7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xD11Ne6B0XURx5hXPv8KTj9x6WPGO9fXqinx2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\na3PJDE0aC2dTun41Cdas8L1hE/Dv0/qgL0FsbcLT5mHf1iKcTXYMOi8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Fbrdz+4NPULK7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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1321baa50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rets.plot(kind='scatter', x='SPX', y='VIX',\n",
    "              style='.', figsize=(10, 6));\n",
    "rg = np.polyfit(rets['SPX'], rets['VIX'], 1)\n",
    "plt.plot(rets['SPX'], np.polyval(rg, rets['SPX']), 'r.-');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SPX    0.555635\n",
       "VIX   -0.021926\n",
       "dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret = rets.mean() * 252  # annualized return\n",
    "ret\n",
    "\n",
    "vol = rets.std() * math.sqrt(252)  # annualized volatility\n",
    "vol\n",
    "\n",
    "(ret - 0.01) / vol  # Sharpe ratio with rf = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RvKwkqmpbSLZxTHxNrBYLLV5/h8+yfV8Ntz22nuLCdP4vZmi2/dqQ/XkPOgve\nehRwGYZxAXCqaZrXGoYxGpgArASWAP8ATo28J/L3LZH9HMB0YJVpmvWGYTQZhpFvmmYlsDRmHxER\nkRHB4/fxhzXPkpTu7VBc1Of3U28/AMBV8y/l5OljmVCYzi8fXd/hOLYEk9pbNTT7gMSlHRKZPDaT\n7ftrOVjVmHD71ZfM4sHntrHshHA9r5s/u5Cqumbys1N6dPzhzmq10Ozx89t/b+aEqaNYEln7sfVp\nwtbljyBcPqKmwcP4/DTOWTSOCQWDk+HryVOK84G/A+8ZhvEakALcD9wE/NIwDAOYBNwAYJrmWsMw\nXjMM46eEn1K83jTN+sjhrgB+ZhhGCeGiq3/t348jIiLSf57fuo6Vpf/G4QivOwjgCUSyI0c638+3\n32DB6aMBGN/JD/TYGlDtvfHhIQCc3dR2apXbTXmH+dPymB9TwDMj1UnGMTB/q5XNaqGyppnKmmbe\n214ZDbhCCeZ1vbh2PwD7KxtZOmf0oF1jTybNf0D8EGGsr3ayz52dtG8EvtTjqxMRERlCb+3dRCjZ\ni685jTE5GZRVNxEMJBHyJOMrm8gtV5wct4be25vKWblmP186d2F0Mnuyy84vvnoytz+2niZPIG7C\ndk2DJ67ye6tVG8IBl7vF16PrdB4DFeOPRvsSGMFQCKvFgqfdYt0AH+yo6tA2GFT4VEREpBMWR3ju\nVfO2BZRZ0zsUvFy1tp4rPzI9+r6lsY6QJ5XCnPi6TPnZKfzya6cQDIbYc6ieXzzyAQBPv7WHz58/\no8N5pxVlsuNgHRcuKe7RdTqP84fL2g/PVtU2U5Cd0mGxboBRWcnsPlTfo+Kz/en4DolFRES6UBes\nJBSCkM/ZIdgCONBuzlRtY3i4MVHWymqxYLdZyYnZZu6vTXje1oxNT8s2OLtZx+9Y174w7MHK8Ncl\nUYarte+1H5838BcWQxkuERGRBOqamwg6GwnWZ0MoPoP0iTOn8MLafewuraepxRctOVBd14zNaiEj\npfNAyR4z/FdR08zDL5lYLBYuP2datN3jC2C3WRNWi09kalEWLoeNi5YW9+ITHjvaZ7j2lNXzwrv7\n45Y9qm/ykpHipCHyhGJW2uBW2T++Q2IREZF2yupqeHXHh7xbugGAkKfjk3wfOWl8NBg6Uh8edgwG\nQ5RWuRkzKrXLQKn9tlc/KOWV9w/i84ezMdv31bC3rHdlCpJddn57/RmsOHlCr/Y7VjjaZfief2c/\nu0vr2R59NmFlAAAgAElEQVSTQbz1r+E67vVNXlwOGy7n4A7DKsMlIiIS4xdv/w6/q22xwxxXFhUJ\n+i2eUcBL7x3AH1kqp6KmCa8/yLj8tC6Pn5Hi5IIlE1i5Zl9cuz8QwmGHf7+1N/JeSw33VE/msB2u\nC6+v1NDkIz2lZ+U2+pMCLhERkYhNpfvwu2oIujMI1ORDyMryuUuZe8YYcjOS+Mrtr0f7ti4U7feH\nSw9s2RuuE1GU13XABfCxMyZjHqhl18G6aFvr4sutCbCLj9Phwb5w9XAOWygUoqHJy7j8wa+ur4BL\nRESOe4fqjrChbAc7anYD4K8cR6BqHJ9cPoWzTxgXXfPwinOnRctAtK5b2JqJeiNSymFCYc9+mLev\nEdV6HK8/iN1m4dLTul+fT8J6Uq8sNclOsyeAPxAiQxkuERGRwbWt/AD3bvotFlvbU4hBdyYAZy0o\niltgevn8oujraIarNVCKPBE3fXxWj87bvianP7K+X7PHT5JTP557o6uAa/r4LDy+AAcq3TQ2hyfM\npyngEhERGVyr9mzAYvMTqMkjUJdHyOti6RSD0bkp0aAqkdZtvkCQUChEndvLhIL0uACtK+2fZPRH\nhhRbvAGSXcd3Xa3ecnVR+PXGz8zntkc/wB8I0uwJB8VJjsEPfxRwiYjIca3OE34i0F82kWBjDgBn\nXDyGyWMyu9yvdUix/EgTzZ4AXl+QzLSeL5fz0dMnEQyF2Li7Onz+QJDSqkZqGjw9XkNRwrobUmzN\nGNa5w0+Utn+qcTCoLISIiBzXGn3hIpkhX7gukwUozOl+UefWDNeTr+3mric/BIhb5qc7RflpXHPZ\n7Oh7fyDIX57fHr6m5p4t6SNhnRV+/cZH5wBQlB+u/N9aaHYolkJSwCUiIse15qAbaAu4ls4ZTWpS\n9xmm2OHG3aX1ANQ2eHp17thj+AOhhEvRSPc6KwvRumD3hMgC4jtLw0+F9nRR8P6kgEtERI5r3lAT\noYANguEfwp8/f3o3e4Q5EmRJPn32tAQ9O2exWCiOPNUYCAQJtp9JLz3S3dJGrQFXaxmOochwaQ6X\niIgct/714Tv4XbVYPalcdf4MWnyBHi+nk5uZFPf+s+cZPS4JEWv+tDxKyhvw+oNkpjopq27q9TGO\nd91lrEZlJZOd7qImkoFUhktERGSQfLB/Ny9XPwXARNdsTps3hnMWjuvx/hMK0uLqOSX18Yd46xIz\nHm+A4sIMoG3ukfSMK8G9b/+EaXrMgwi9mWvXXxRwiYjIcem9A9uwWGCSZRHfPuOyXu/vsNu47Wun\nRN/3NWvSGix4fAF8kZpeo9plz6RriYYIv/mx+KA1JaltUK94dMaAX1N7CrhEROS4tKMh/ETg4qLZ\nXdbb6kp/DE21Zls8vkC0iGpfr+d4lejrMGdSbtz7ZFdbwJXiUh0uERGRAVfX3ESLsxKA8dkF/XJM\nj8/ffacEohkubyBabd4+BJO6R7LYSfPTxmVx6pzRHfrEBlmOIQhoFXCJiMhx57mt7wLgrx7NuJzs\nozrWtR+fy8o1+zhxal6f9m/NcLV424YUhyIgGMliy0J8/dLZZKR2LECbHDOkaLf37MGI/qSAS0RE\njjsfVG4AF3xj6Ud7/FRiZ+ZOHsXcyaP6vH9rza/GZl906RnXEFRCH8liJ8139vWMzXDZrMpwiYiI\nDIiVW97l9dJVjMpy4XYewu7JZM6YCUN9WdHlgGobPRw63EhmqpOUHhRelTaxQ4q2HgRcQ0EBl4iI\njGhNXg8NvgbsXWQt/EE/Kw8+jcXh42CjFYJWCoOzO+0/mNKSHdisFsqPNFFd72FW8dENcR6PYocU\nOwu4YocUh4ICLhERGbGCwSD/++ovCTobu+1rcYDvwFTmpp/MBzuqmHby0Ge3IFxtPjOtreDp2Ly0\nIb6ikcfl7MmQ4tBmDRVwiYjIiHWw5ghBZyPBlmQWj5/RIbvR4g3Q0OSloclHWaUXf0UxH5RVAUSX\n1BkOMlNdHKkPV0Efm5c6xFcz8sTW4ep8SHHwi53GUsAlIiIj1s6qUgACRwq59LTLyE4PL0Dd0OQl\nFIK7ntjAvoqGhPu2zp0aDjJjnqrLSnMN4ZWMTLHrWlosiQOuJM3hEhER6dof1zzPhvq1OJ3WuGVZ\nmr3hrFCoJQ2vLxBt//Y9b3V7zOE0MT32MyVapka61lmQFWvMqFQmj8lg+fyiQbiijvTcqYiIDGvb\nyg6wvvk1go4mmluCWEJWvN4QNosNu8VF0J1BoC6XdWYlDU1egsFQj46bOsSTqGMdqGqbg+ZUSYgB\n4XLYuPnKhSyZXTgk5x8+320iIiIJrD2wDQB/dSG+3SdwKNK+ZFYBuZnJPLulBIB/vrGHtVsruOJc\no0fHTR1GGa4rzpnGLx9dDyjDdaxSwCUiIsNasz/89F7g8Ni49jVbKjr0PVjl5u+v7Ixrmzs5l427\nqzlhyijmTMohK93FzAk5cfN+htqEmAn8CriOTQq4RERk2AqFQpS1hCfGTy/KZ2td98OFJeUNTB+f\nxfb9tQB882NzeN+sYu7kXJKcw/PHXmyQ1R8LYh+Pbv7sAjwx8/iGm+H5nSciIgI89eEaqtkHwNis\nbLZypEOfZJedZk/8wtFnnDCWJbMLmTg6A5vVyuIZ/bNA9UCxWCxcvLQYd4t/WM0tG0kmj80c6kvo\nkr6qIiIybL1fuh1SwvO3CiblQiTgGpWZxOG6FgAWTc9j1YayuP2SXXZOmjm8g6z2Lj1t0lBfggwg\nBVwiIjKgmrwtrNz6DpOLM2hs9NDi9VPb6KUwJ6WbPUO47eFAylcyi4w5bfWpzl44LjpXqzCnY6HQ\n2DILIsOBAi4RERlQ97/xHCW2d3i9/Wjg4R7s7IRgcyoEHHFB1Ni8VC47bSL/enMvsybmwGvxuyUP\ncZFLkfb0HSkiIgPmkXWvUGJ7B4AJwZNJIoVNe6rj+lx9yay498EQlJTV89J7B8Lv3ZkU5qTEzdEp\nGpXKzAnZnDm/iLRkBydMGcWHu9oiOGW4ZLhRwCUiIgNmTdUacEGwMZOyA3nUNfiA0XF9TsybF7fg\n8N1PbmDjbn+03++uPyP65N74/DQqaprJSHVisVhISw7X0vr6ZbP5yu2vR48xnGpsiYACLhERGSA1\nbjdBZz2Wxhw8WxfjwZewX7PXHw2QfP4gG3fHZ8BiyyTcfOVCgqFQh6Vc7DYryS4bzZ4ANquF5CFe\nqFikveFT9U1ERI4p6w7sxGKBcWnjopmoWKdEllhpamkr6VDn9sT1+d/PnBj33mG3dloY1GEPt6en\nOHq0tp7IYFLAJSIiA2JzRfgpwqk5E/jEmVM6bG+d2B5bQ6u2wRvXZ2xeWo/P1zoqmZ+V3NtLFRlw\nGlIUEZEBUdK0i5DDwrIp88hJTaPB42fd1gr2ltUDRJfW8QWC0X2q6poBuOz0SSyZWZAwM9aZ2sZw\nsFY8OqO/PoJIv1GGS0RE+t3ewxX4XTWk+AvITUvHYrFw5YqZfHJ5W6bLbgv/CPL72wKuZ98uAaAw\nJ4VRfcxUjcvveVZMZLAowyUiIv1uw6E9AIxPKY5rn1qUycVLi5kzKZetJeHCXP5g2/qIreUc5k7O\n7fU5LRYIhRRwyfDUbcBlGEYBcCswzzTNxZG2+cB3gXXAScDtpmm+E9l2A5ABZAEvm6b5TKR9HnAN\nsBfIB643TTOIiIiMaIFgAH8oQCgUYlPpPrzWRl6ufBqsUJA6Kq6vxWKJLmGz40B4cenYDJc/ECLJ\naet0YnxXfvSFxZSUNTC+IP0oPo3IwOhJhmsp8DQwL6btVuD3pmk+bRjGpcCPgPMMw1gMLDNN80LD\nMGzANsMwXjdNswF4GFhummaVYRh3AJ8DHuzXTyMiIoOmydvCo+v/y3r3qo4bIxNWFhRN73T/6JBi\nzByuFq+/z0VLi/LSKOrFJHuRwdTtHC7TNJ8CGto1lwN5kdd5wPuR1xcCayL7BYBtwBmGYUwCkkzT\nrIr0Ww1ccHSXLiIiQ+mfG96KBlshv4NAbR7+qrF4900n7ciJeDedxpSC/E73tyeYNN/iDZDk1GwX\nOfb09bv6FuDvhmEYwMnAtyLt+cDWmH71kbbDxAdtre0iIjLCtPi83PraH6m3VIINfKWT8JdOofV3\neIsF7P4UkjspdNrKbgvXcQgEwnO4dpfW4W72k6eyDnIM6mvA9R/ga6ZpvmsYxmzgv0AhUAnEDp5n\nRNoqI6/bt3crOzsFu10VgwdTXp7mPww23fOhofveNy9u/JAaewkAwZYUfvqxz/Hsqv28+WEpEJ64\n3uwNkOSyd7jHse9zslMBeOyVnTT7Q2zfd4RgKMSnzp2ur00/0r0cHnoTcMWW7S0iPKxI5G9n5PVK\nwtkvDMNwANOBVaZp1huG0WQYRr5pmpWE54Wt7MlJa2qaenGJcrTy8tKpqmo/giwDSfd8aOi+951Z\nGl5U2rtvOoGKYmynWphYmMabMX3qGjzk56TE3eP297w5UlW+qcXPoy9uZ3xBGnablSmFafra9BN9\nnw++zgLcnjyleDrwWaDQMIybgDuBrwA/NwxjIzATuArANM21hmG8ZhjGTwk/pXi9aZr1kUNdAfzM\nMIwSwnnnvx7NBxIRkaFxuCn8dGHIEx76y0h1Mq7dZPUQUHGk61+YczOT4t5X17X0ecK8yHDXbcBl\nmuYqoP0jKE9H/iTqf2cn7RuBL/X2AkVEZHip89WBBULecMBkt1kpyEnp9XGKC+MzAe4WP7kZrn65\nRpHhRpXmRUSkV9yB8MBFyJuMK5KRSkt28Iuvnsx5i8f1+DgWi4V57QqcuvSEohyj9J0tIiJdCgaD\nlDdWY7OFnyZsCtYTClq56+ozccYUKM3PTmFizDqGv/7Wqd0e+6oVM7j23rei710O5QHk2KSAS0RE\nuvTA2y+wyft6W4MTQs2pZKZ1HP6bNTGHxTPyOXFqHukpzg7b20tNjv8x1JcK8yIjgX6VEBGRLq0/\nsBsAf3Uh/soi/JVF+PYnriCfmuTg6ktmc9LMgh4d22a18v8+2baQiQIuOVYpwyUiIp0KBoNYksJP\nG/pKZkHAAcA1l83pt3OkJ7dlwlx6SlGOUcpwiYhIp/763svYMqsJNqdAoO139AVGXhd79U7ssKIy\nXHKsUsAlIiIJVdTXsM79CgDenQuIr3/df9KSHdHXzR7/gJxDZKgp4BIRkYT+se5dAEJBC//v0qWc\neeLYATlPbFZrw+7qATmHyFBTwCUiIh0Eg0H2Ne0BwLt7HrOKcyjKSx2Qc1ksbZmzay6bPSDnEBlq\nmjQvIiIdvL5zE+6UvQAUZYXna508q5B1ZhUrlkwYsPNOGZs1YMcWGUoKuEREpIOSmnIAXJ48/u/j\nZwGQ7LLznU+fOCDnG1+Qxv6KRpJdmjQvxyYFXCIi0kG9twGAs8cvw2od+Nkn379yIYFgKG54UeRY\nooBLREQ6aPA2gBVGpQ7OEJ/dZsWu5JYcwxRwiYgcp17buYFafyXpqR2X4KkJlQEwLjN/sC9L5Jik\ngEtE5DgUCoV4suTvWGyBxB1sEPQkkZeRPrgXJnKMUsAlInIcavZ5sNgCBN0ZfGHBxSQ5438cPPyS\nSWNNMnabqgeJ9AcFXCIix6GapkYAgi0pjEuaQmFOStz2UF0tyZbQUFyayDFJAZeIyDHi1R0fUu2r\nICvN1W3fysba8Au/g8YmH+S0bfN4AxyuayEjwdwuEekbBVwiIseAyvo6/rH/MSzWEFT0fL+QN4n9\nlQ1MKcqMtj3wzBYA6t3e/r5MkeOWAi4RkWPA5rISLNYQwcZMpjkWcc6icV32P3TYzROv7iHYkM1q\nylk+vwgAfyDI+p2HB+GKRY4vCrhERI4BdZFCpf7DY9hUaefaj0zvsohoY0U5wfp6APaW1fPSewc4\nd9E4Dte1DMr1ihxvFHCJiIxQwWCQn7/2MJWh3VhsQQBCvvD8rVv/to6vXjKb/KzkhPvurwxPmjfG\nZWEeqOXvr+zkYGUjC6e31d2aPl7rGor0Fz3vKyIyQv1p7QscsmzGF/DjabYRbMwk2BgOkvaWNXD7\nox8k3M/d4uPV9w/icthYvqAo2v7WpjLufnIDABcvLea6T5ww8B9C5DihDJeIyAi1pW4TIbuFls1L\nwZcEQH5WMpW1zQBU13sS7vfNu98E4IQpo1ho5JHsstPs8cf1yc1MwmHX7+Qi/UX/mkRERqA3d23F\n56wm1JIaDbYAvnzRTO6/7nQACtrV1oJwhflWhbkpWCwWvvWxOR36KdgS6V/KcImIjDDv79/NY/v+\nisUCIXcWP/3ySTQ0+WjxBpg0JgOLxUJ+VjIer7/DvrGZrPMiTzLGloRo5VCFeZF+pX9RIiIjzJp9\nm7BYQvj2G+Q1nsTo3FSmjcti7uTc6JOJTocNjy/YYd+GJh8Ap84dTWakQKrNauV7V8yP66clfUT6\nlzJcIiJDqNnnYWvFXjJSHT3ep8yzH6xQ5JzM1SvmJuzjcljx+jouTN0acKWnxJ9valEWd31jKdfd\ntxoAu4YURfqVAi4RkSHy7Oa1PFf+VLg6fG9YIRSw8bUVi8nrpOyD02EjEAzhDwTjslXlR5oAGJXZ\ncb/YBaw1pCjSvxRwiYgMkTWH1mGxhwh5XZxUsJDcBEFQIu9tr6R0n4PkUzvPirkcNgDWba/k/R1V\nfPGCGSQ57RyI1N8al5/WYR+noy3I0qR5kf6lgEtEZAgEg0Fq7fsIBS20fLiMLZnJFBem8/XLOj4x\n2J757noO1NXgcnQeFCW5wgHXA89sBWDsqFQuPW0SByobsABFeakd9omtTK85XCL9S/+iRESGwIYD\n+2PeWThc18I6s4r6pu4XjG7xBbBaLF0GRYXZ8SUh3t1WSWOzj+37a8nLTo4bPkxEGS6R/qV/USIi\nQ6C8MbxAtL9sUlz7b/61Ofr6QGUjv3jkA+oa2wqY+vxBdpfW43LaulwrcXK7Ug91bg+vrS8FYOrY\njmUgWp02dzSzJ+WQn92z4U0R6RkNKYqIDIFKdw0AIU98YFNa1Rh9ffeTG6hp8PD06hKuPM8A4EhD\neHHp3AxXl8c3xsWvg+j1BamuC1egX7FkQqf7XbViRg8/gYj0hgIuEZEh0OQNBz9nnziRpeNP5LX1\npbz2QSknTBnF7/+zhbRkB/Xu8PCizdqWyWpwh8s6zJmc2+Xx2w83BoIh6hrDx0tL7nkJChHpHxpS\nFBEZAk3+cMCVk5pOUV4aZ0cWkV69uZy1Wyt45f2DBILhchHOmPlUtZHhxfRkZ7fnuO3qJXHvzQO1\nWIDUJAVcIoNNAZeIyCDz+n2UtOwAINMVntzeWsYhEWfMtg27w3O/Jo3J6PY8o7KS+f0Ny6J9W7wB\nJhSmY7V2PvdLRAaGAi4RkUH2rZW3E3Q2AFCQkQ3EB1XttWa6fP4gH+yoIifDlXD9w0Qcdit7DtVH\n33/67Kl9vWwROQoKuEREBpHH58OaVgfALM5lQm4eAMmuzgOu1iV6KmuaaPYEmD0xF2sXTyi2d+aJ\nY8N/zx/L1KKsbnqLyEBQwCUiMohKjlRisYTwHx7DFSefHm23Wa3cf93pCffx+cOLUB+uCz+hmJeV\n1Ktzfuqsqdzx9VP47LlGH69aRI6WAi4RkUG0t7oCgOKcAjJS4ie+J7sSPzjuD4QDrur61pIQvQu4\nHHYrOb3cR0T6lwIuEZFBEgwGeevguwCMzyro8X7+QAivL8BTb+wBEi88LSLDW7d1uAzDKABuBeaZ\nprk40uYArgcagNnAYdM0b4lsuwHIALKAl03TfCbSPg+4BtgL5APXm6YZ7PdPJCIyTG0pO0CNfQ82\nbyYXzjqpx/vVNLRw9Z1vRN+PHpXSRW8RGY56kuFaCjzdru1/gbdM07zfNM2vAY8DGIaxGFhmmub/\nAdcBdxqGkR7Z52HgZtM0fw4EgM/1xwcQERkpqhprARifNJnM5I6LR8c6Z+E4Pr5sMgD7K9qqz19+\nzjTV0RIZgboNuEzTfIpwJivWZ4CJhmF82zCMHwPlkfYLgTWR/QLANuAMwzAmAUmmaVZF+q0GLuiH\n6xcRGTHqWpoASLF3PiR43Sfmcfk50/j02VM5c3746cJmrz+6vfWJQxEZWfo6h6sYCJmm+WvgDeDJ\nSHs+8cFZfaSts3YRkeNGeWP4d84UZ+cB15xJuZwVqTrfujxPKFyGiwuWTFDRUpERqq9rKdYBayOv\n3wJONQzDAlQC6TH9MiJtlZHX7du7lZ2dgt3eeX0a6X95eendd5J+pXs+NAbyvrf4vJQ3VGG1QjAU\nwhfws9nzFgCFWTk9OneoNdKKmDMtf8R/r4z06x+JdM+Hh94EXLG/Vr0CTAJ2Es527TZNM2QYxkqg\ndfK8A5gOrDJNs94wjCbDMPJN06wkPC9sZU9OWlPT1ItLlKOVl5dOVVX7EWQZSLrnQ2Og7/sNz91N\nc9KhDu0hv50ix/g+nTvo9Y/o7xV9rw8+3fPB11mA25OnFE8HPgsUGoZxE3AncCPwQ8MwTiQcVF0B\nYJrmWsMwXjMM46eEn1K83jTN1jUlrgB+ZhhGCeGhzL8ezQcSERmugsEgTfZK8DlZWDiXtGQX+8sb\n2bG/nkDlODLm9a2sQ1qKJsuLjFTdBlymaa4CVrVrPgR8pZP+d3bSvhH4Um8vUERkpKl2N2Cx+wnU\n5LM44yzG5KZyw1NvA6MBSOqkwGl30pIVcImMVCp8KiLSz2qb3QCEAnYamny88v7BuO1pvSjrcO3H\n57btp4BLZMTq66R5ERHpRH2k/AN+O29uOBR9yvAzZ0/lhCmjSEnq+X+9hblt9bpan1oUkZFHAZeI\nSD/x+r1sPLSXzYd3ABAKONheWhvdftaCIiyW3pV1GKU1EEWOCQq4RET6wapdm/l7yUNYrG2lHEK+\ntsWps9KcvQ62AKxWC1eeZ+ByqjyOyEimgEtEpI/+vfFtqnyl5GQk8eq+1VisIYKeJAKV4wj5nQSq\nR0f7phzFcjzLVF1eZMRTwCUi0geBYICXKp7BYguES0FbIeRz4Nm8FAIdg6t0TXgXOa4p4BIR6YPf\nvP00FluAQO0ofAenAhZsvjQIJJ7YvnC6VjMTOZ7pkRcRkV7aUXGIbS3vAhCoHkOoKZNQUwZOa9uc\nrTGjUuP2OWV24aBeo4gMLwq4RER6aeX21VisQbx7ZhOoHhNtP++k8Xz90tlkpDi49uNzmT0pJ7ot\nuY/FTkXk2KCAS0Sklw57qgH4zkVnc/Ksgmj7RacUs3B6Pnd/6zRGZSZzxbnGUF2iiAwz+pVLRKQX\n6pubqeUg+B1MGlXA1AtHM3VsJjOKczr0zUp1JjiCiByPFHCJiPTCOyXbwO6lMDgTuy1cG+vM+UUJ\n+zodNj5//nTysvq2WLWIHDsUcImIdGNTaQm76vaQk5HEltpwFfnRKaO72Svs9Hljuu8kIsc8BVwi\nIl3wBfz8ZuOfsbpaoLytfVRS7tBdlIiMOJo0LyLShZd3rMPqaiFwpIAvzLycExzn4tm+iAlpE4b6\n0kRkBFHAJSKSQDAY5I2dG1lZ9hQAvoNTmZE1k/070rE0jqJ4dMYQX6GIjCQaUhQRSeCuN59kT+B9\nAPzlEwi1pHH73z+ktMrN4hn5ZKe7hvgKRWQkUcAlIhLj3RKTv+14hKDNg8UCY1uWkJk0hfepYl95\nA6Cq8SLSexpSFBGJCAQD/GX7Q4TsLYRakvEdmsRNKy7DZrPE9ZszSRPmRaR3lOESEYm4582nsDi9\nAHi2LsEYkweA3db2u+mozCQsFkvC/UVEOqMMl4gIsKl0H7sC7wHg3TsTAg4uPW0iAEV5adF+wVBo\nSK5PREY2BVwiIsD7B00Ago2ZBKrCleOTnOFBgHMXjcMayWo1e/xDc4EiMqIp4BKR454/GGBd7WoA\nTsg4mdb/GrMzwk8iWq0Wzl4YDsK8vuCQXKOIjGyawyUix73HP3iDkKMZhyebz599Ol88zUazx096\nSsfFpzV9S0T6QhkuETmuBYNB1lS/QSho5RsLPo/L4cBus3YIthYY4Qn0l5w6cSguU0RGOGW4ROS4\nVl5fS8jRTLq3iCn5nS9IPbUoi19/61RSkx2DeHUicqxQwCUixzWz8iAA2c7ua2slGmIUEekJBVwi\nctz689oXWFezGosTRqfmD/XliMgxTAGXiBx3/MEAHx7YyfvuV7E4IRSwMr9o2lBflogcwxRwiciQ\nqm9u4uZVtxN0uLEwOI8AhmgrXuqvGM+3T/0kM8fmDcq5ReT4pIBLRIbU+wd2EXS4CXqScIbSGDsq\nFbvNSvkRNxaLhYLsFBqbfTR7/ORlJffDGUPsOFAHfichbxJLC5cyc5yCLREZWAq4RGRIHaitBMB/\naDKeqnHU5KRw46dP5Pr7w4VILzx/Og++tR2Ar3xuIRNHZxzV+WobPfy/l1dH36cXHd3xRER6QnW4\nRGRItfg9AMweX8iUsZlUHGli857q6PY3N5VFX1fVNh/1+WKX5plQkM7y+WOP+pgiIt1RhktEhlRL\nIBxwTR6dDam57CqtY1NMwFVT3xJ93ddldVq8fkIhSHbZafEGALj0jMlcvGTCUVy5iEjPKeASkSHl\niWS4Uh1JkBT+L2mdWRXdXl3vaevrC/TpHF//1SoAHvjOsmiGKyVJBUxFZPBoSFFEhpQ36AMg2ZnU\nIQgqLkyP79vHgKvVM6tLYgIu/b4pIoNHAZeIDJnvvXQfpWwGIM2ZREZqWyX3Gz99IqNzU+L69yXD\nFQq1lYDYtr+GZk/4GKkKuERkECngEpEh0djSQp1tPwQc+A+PZlxWPllpbQHX9AnZHZbS+c/qEiqO\nNPXqPK1ztgAyU5w0e8MZrmQNKYrIINKveCIyJPZUl2GxgP9IAb6S2WSmJpGeHGLu5FzmTAqva5io\n7taeQ/VYrBaqapqpqm1m2YmdP2VYVdvM8+/si773+APRIUVluERkMOl/HBEZEnUtjQCEfC4+c/ZU\nAKxWC9d+fF60z5nzx/Kf1XuZPCaTxTPzeeA/W2nx+vnu79ZE+xTlpTGlKDPhOe7550ZKq9zR95v3\nHCYdNM0AACAASURBVOHQ4fB7TZoXkcGkgEvkGLar6hDYvdisg7NkTm8caDwIwMyiAs5eOC5hH6vF\nwp3XLMVigU17jgDQ0m4elz/QeamI2GDr0tMm8u8393Ik8tRjdnoS+P2d7Soi0q8UcIkcgx5a9zIH\nmkoo9e8c6kvpVoottcvtdlt4qmmy0wbEFy6N3d6qrNrN1pIaGpq8ce2LZxTw9uZyKmvCxVNzMpM4\nUt14VNcuItJTCrhEjjF7jhzgnfqXAQi2pBA4UsCKk4e2wOfOg3XsPFgLhOdOnTp3DIfrmnl/aw1j\njEk9OoYrEnA9+/a+uHZfuwzXH5/dxt6y+g7752clM3dyLv9dF86sDcesn4gcu7oNuAzDKABuBeaZ\nprm43babgWtN08yLabsByACygJdN03wm0j4PuAbYC+QD15um2bey0SKS0O2vPUZJaD0Avv0G/ooJ\n5KQnc+mUpUN6XX82t7HtYHiJnjpg5S6YMWEM/rIaUua4enSMzNTE/Xz+8H8jLV4/j7y0Iy7Ymlmc\nzejcVBw2K1arhaRI0CYiMth6UhZiKfB0+0bDMM4AsoFQTNtiYJlpmv8HXAfcaRhGa+XCh4GbTdP8\nORAAPneU1y4iMd4r2cne4HpCARuBIwX4y4shZCWmDFVUfZOXh140+cvz2wf0mgLBIL9+cgNvxayH\n2Grbvhqg45BgZ7LTXRTkhOtyXf/JE/jYGeHMWGvAtX1fLas3l8ftM2VsJpefM41PLJ8CQHNLeP5X\nskuBl4gMrm7/pzNN8ymgIbbNMIx84BPAPe26XwisiewXALYBZxiGMQlIMs3oeh2rgQuO7tJFJNYT\n21disYB354l4d50IhIfMGpt9Hfre8diHvLa+lFUbDnEkZq3C/vbcmn1s2B1eF3FsXuK5Wsmuns9s\n+MkXF/OHG5cxa2IOKZH9WifNH2mI/xwfP3Nyh6FUuz18Twpzup43JiLS33o9h8swDAvwM+B6whmu\nWPnA1pj3/7+9Ow9vs7oSP/7V6n2PY8dJ7Ow3IXsgISzZoEBZWpYuFFroAu3AwLTTMqVM+6N0n05b\n6EzL0HZKC3Ta6ZQWWspe9iVJCQQSyHazOovjeN8ta//98b6SJVvyKkuWfT7PkyfSq1fS1bUsHd97\n7rnt5rFGooO20PFBFRVlY7fLX6PJVFqaN/hJIqFG2+cen49u50kCPdkE2qcA8In3L+Tvu2o5eKIN\nlz9IZXk+AC0dPZxo6E0WdwfH7me+edcprBb48nVnsHh2CTsPNnLgeAt/ffUwAF+6dhXnLp+Owz78\nGszFRcZoV2aWk9LSPNz+3qG8WdPyuf6yJf3u85nLl4LVyjUXKkDe66kgfZ580ufjw0iS5lcBHuAf\ngGIgSyl1O/AoUA9E/mTzzWP15uW+xwfV0jK8qtJidEpL82ho6Bj8RJEwiejzx9/dZlzw9daW2rR8\nGnYLHDzRxgtvHOWys2cRDAapPhX9XEdrWplWkDmq54+lsc1FQ4uLBTMLURX5+NxeFs8sYPHMApZW\nFZHhtDGjNJfWlq7BHywGl8tYhdjc2k1DQwcnThm5W5tWTefStVVx+/TqjXMJmNXm5b2eXPL5knzS\n58kXL8AdTsBlAdBabwe2AyilqoAbtNY/MK8/CdxpXnYAC4FXtdbtSqlupdRUrXU9Rl7YkyN8LUKI\nPlp6jA/UEsssai2wsLIIi8XCinnGaNfOg40U5Dh54Ol9bFhRAcD8GQUcONFGe1f/KcdEePDpfQSB\nMxeX9btt7vTYhUqHI9NhfHz1mMFTfasLC3DN+fOHnBcmhBDJMpRViuuB64BypdRXgbu11m6l1Fzg\nJiDTPP5jrfUbSqmXlFLfxVileJvWOrRk6BPA95RS1Ri5Yw8l/uUIMTl5A8Zoz5KKGdx18TosZsWD\n/Bwn00tzOHSynUMnjV/FV3acBGBKQRYHTrSFA5ZEeXNfPU9sqeZ4fSeFuU42roi/9c5ohBLfXW4f\nJxo6OVTTzpyKfAm2hBDj0qABl9b6VeDVGMcPAV82/0UevzvO47wL3DiyZgohBtLjM6qnZ9oz+uVD\nLawsiqq4HlKYZ2wM7fb4+902Gu8caOB4vZEj5nSMXf5lKNn+he01ZJjPs2bhkFJDhRAi6aTwqRAT\ngNtvBFzZjv61qiqmxF6RV2jWtXJ7ExNw1be6OF7XGRXAdfeM3dY5oVWKLrePR14xkvDLS2T1oRBi\nfJKAS4g09cd3XuPluufJy7HT5esBC2Q7+ye/ZzhiT7EV5pkB1whHuF7YfoJDNW3c+IHTsFos4Q2l\nZ5Tmhs+JVZIiUQrzMqicmsux+t4Vl8V5QyuiKoQQySbJDkKkoUAgwEv1z4LTRXu3G5/bgb+9iPKc\n/lNqGXGm9YpyjeBk865TUWUihup3z+3n73vqqK6NXgHVNIZ1vSLZbVa+8Zk1zJ3euwC6OF8CLiHE\n+CQjXCLl2rq7Odx6jJwsx+AnT0D1wWxaW4dX/uTB7U9gcXgIdOXh3m1s2zOjNIc5ZcX9zo0MuMqK\ns6lrNp6rqrx3JGrze7Vcfd78IT+/P9C7K9euI01UTMkOX++7ufRYy3QaH2M2q2VYRVSFECKZ5NNJ\npNz3X/8V7fbjqW5GerEaG1N7DqwMH7ruIoV1kA2Z/+1za9m2tw6fP4DDbuM//ulc/vmnr3OycXgB\n36Nm4VKA1k4PLR3uqNvtNis+f4CzFpcP63FHIsvcHzErw47FIhtSCyHGJwm4REp5fF7arDVYAFuD\nYumcEqwWKC/OHvS+E0V2TgbdXe7BTwReeqeG9m4PBC0sKVrGusvm8tNH3wOMvQZj8QWiN1Ncs6i3\nLlZ+jrFS8b3DTXT3eMnOjD/K2Nbp5v4n9/LhDXPZf6w1fLyj28OfXzsSdW5xXgbfvnENtiSUaAgF\nWdkyuiWEGMfkE0qk1J6TJ7FYA/gaK3Admc1W83v713ecN+zHeu6t4yyYUUhVeXptYzGUStCdLi+N\nbS4eOfAWAIW5Tj508RLaujzhcwpzYwdcS+cUs355Becumzbgc7yxp45Nq2bEvX3bvnp2H2lmz5Fm\nZk7Nxemw4vUG2B7eIrWX02HFkaQtudrNPsjNnpxT0kKI9CBJ8yKljrbUAhB0RS/nD21IPFS1TV38\n/vkDfPPBNxPWtvHktv/azLceNIKtsuJs7rn1XKaX5obzl4C4BT9tViufungh8+JUd1+/3AjE6lpc\nA7Yh9DMJAl5/gAyHjTNPi64iH8oXC/a98xi6Yt1s7DYLF59ZNfjJQgiRIhJwiVHp8blx+Vwj/nes\n08jdKrBHJ3s/9vqR8MhFQ6uLJ7ZUEwjG/xrvW0uqx+Pjya3V/XKL0o0/EOCBp/bi9fUGoKGkd4hf\n8mE4Pnb+fCwWqK5tH/C85rbevvT6AjjsVj60YW742Ic3zmX1ImOV5FiWg+hLVRbxs9s2cLoqTdpz\nCiHEcMmUohixn29+nPfcryXksWbmTaeJ3kDiya1HeXLrUX586zl8xazvNHd6AYuqivrd1+cP9Kuk\nHrp/dW0Ht1y1NCFtTIX9x1p57d3aqGMl+b21tuz20QdcmU47FSU5HK3rJBAIxky8P9nYxQtvnwCM\nvC+vP0CmwxbOAQNjdCvDnEb0JKiY6lDZrPK3oxBifJOAS4zYzuadWLIsVGbPiZs/FCVojNiEEqk7\nu70crGkj0J3HvHnl7OBwv7ucbOoNwuKtP3vs9SM8ufVo+HqPx0eXWeF8+/6GQZPBx7NO83VMKchk\nVnkexfmZbFrVuzdhWVE211+k4k4XDtWsaXnUNHZxsqkrqnBpyKGatvDlvGwHrR1uHFmOqG2EXG4f\nTqdxPSdN+1sIIcaKBFxiRHafPIY1px1/WzGnF17CBctmDnh+TWMX9z++h5rGLu785BnMnJrLXb/e\nhsesEj51de+qRJvVgt9cWbfzYGP4uDdOXtfJxujRraa2HtwRGzLr462snJ+e0009Zk2ry8+dzTlL\nYye9b1w5+s2hK6fmsZlTnGw0Aq72Lg8NbS7mVhiBXFfEFj2h0cTSwizAWB3Z0uHG6wtw5qIytu9r\n4JKzJJ9KCCEiyTi8GLbGznbu3Xk/AIG2UuqbXTS2uvjcD19i6+5T/c4PBIPcef8bHK3rwOcPsG1v\nHcFgkFNmLtJ1FylWLSjl4rWVfOPTq1kwszB835ffqQlf9nhjB1w95tY0HzxnFgBN7W5c7t4prb4B\nWTpxma8t0zm2K/7KzDIcoZ/Jv9y3me/+ZjvdPUYuVqycrOpTxsrKr1y7kjWLpnLRmplUluXx/ZvO\nYv3yijFtrxBCpBsJuMSw3bPlf7Bm9OBrKsdXV8nxhk5u//lWfP4gv3x8T7/zX++Tg/TWvnrauzx4\nfQEWzy5m08rpWK0WPrJxHpVleVxwRu9omSciWdzrj50X1OPx47BbKSsygobGNhc7IkbGupNc+TwR\nDp5o45eP76HNrM+VOcY1pspLjL6ra+7G6wvg8xsjjJ0uL3957TDPvRW/MO3UomxuunxJ2k7bCiFE\nMkjAJYZl/6ka2uxGvtSqvHPJcDjZf7w16pwtu6IDrNomY4RpUVURF5wxk7oWF3953Si4FWsrlhXz\np3D3LedE5QcBeOOOcPnIdNooKTCSyY/XR+8LGO9+49n3frudrbtP8fTfjwG903djZUp+JlaLhYa2\nHp7ddix8/GBNG3/dXB21SjLkcx84bUzbJIQQE4kEXGJYdpw8BIC/uYzrNq5iZowE6/uf2BtVR8tt\nBjzXvG8+axcbdZte2XESgOyMOBsr52VQELECDuLncHW7fWQ57eHVe6HHnj3N2NTY40vuirnR6luD\nrCDXydQxDrisVguZThsn6jujtu1paoveiPq/vrg+fLk4YrWkEEKIgUnAJYZlR6OxjcyHFl5Idqad\nqUWxA4FQXax9R1vCeVjFMYKoyMKdfS2ZUwLAwkojp6srRh5RMBiky+UlJ8tBUX70SsnQc8XL/RqP\nfP4A3+pTvDVZJRYyM2zhfLiQZyJGuyB6RLJsEm2/JIQQoyUBlxiyhs5W2uxHsbjzOf+0xQCUxQu4\nzC/uh57V4WNZGXYKczOYP6O3hEFOZvyA65rz53PzFUv4+IUKgAMRpQlCejx+fP4gedkOrH02Ls40\nR888MabDxquTjV2c6FNTbMGMwjhnJ1Yo+HXYrdxw6SKAqMUHIWsWTWVqYRb5spWOEEIMmZSFEEP2\n9dfuwZoBszIVVrPQZGmcgKvb7aMYY8NjgLOXlGOxWLBY4F8/cTqf+f6LAGQMMMLlsFtZvXCqeZ4t\nXHk+UmjUK1T36abLF/Pzx3YDcMqs4ZXsIpyjEdpe52Pnz+fC1TPRx1rCuWljLZQzV1mWy5yK/H63\nX3fhAgBuunxJUtojhBATiYxwiSFpd3dizTDyeS6Zty58fOmcEmaU5mC3WVg6pyQ8/ffjh3cSCARx\ne/wsmFnIjZdFJ1hfuNpYiRg52jWQvCwHHd39pxQ7zIArN8sIuNYsKuMjG43tZi44YyZ2myVmoDZe\ndXQbbQ1Nh6rKIqYUjG3+VkioBERRXmZ4T8SQuz61OiH1voQQYrKSES4xqL21Ndy79z8B8DVWcNp5\n5eHbcjIdfOuGM8PX7/iFsQ1PS4ebF7afIEjsacOPbprHBWfMHPLoTV62k+P1HUal+ohtXLrCAVfv\nc1y8toqNK6eT6bTx4jsnOHyyHZ8/EHdz5/EiEAyG612FAshkCk0DZzps/bYMqizLxWKJV+tfCCHE\nYCTgEjEda2rkv998lFbbMYI2Y9Ql4M5ilm3ZgPfLjBgZ+f0LBwBo7ey/gbTVahnWVNmM0hyO1LZT\n09BFZVkerZ1uvnTvZsrNxO2+AUooubusKJtDNe00t/cwtWj8JnkHg0G+8ett4fytnKzk/2rm5Thp\n7/ZSmOfEERGcOuxWCbaEEGKUxvef/CJl/vDuc7Q4DxLwWwh0FuCrn4H73XUUWAfeIufmK/rn93x0\n07xRt2euuVfgo68epqXDzd6jLUBvZfScOCNCU8yg7lBN+6jbMJbe0g3hYMtiIWnTiJFuuXIp65ZN\n45K1VVE10JwJ2CBbCCEmO/kkFTEd8b0LQM/us3DvOQtv9RIIWskYZIuZsuJsbr1qafj6abOKUJVF\no27PHLOm1ruHmrjtvzb3q2gfbwru7CXl2KwW/vzaYYLB4KjbEanH42Pr7lO4ElDJvr6ld5PuGy5d\nlJIpxfLibD59ySIynXZsVmOBA9CvAK0QQojhk09S0U97TzcWm7myzxtd26oob/BpwMicrelT+hdG\nHYmKKTkD3h4vQJlalM2iqiIa23oSXo/rjy8d4peP7+Gpvx8d1ePUt3SzdXcdYFRvP3tJ7E2qk8li\nsYQDrYFqpQkhhBgaCbhElCMNdTy730h89zVUANG5OyvmTRn0MSKLY04rSUzelNU6cA7RQCNCoRV3\n8SrVj1RDq1HCYU91MwDdPcMf6QoGg9zwnefCG2wvmlWcuAaOks9njAiO9bZCQggxGUjAJaLc8dQ9\nvNz4LADzSiv4/IeXcecnzwjfPmta3qCPETntONjI1HD8v+vPiHtbUV5G3NtCIzWx9gMcjVDeWKfL\ny5Nbq7n1P15FH2sZ1mMcresIX/7wxrn9KvGnkjJLfCyeNfopYSGEmOxkrkCEbT60h2BmOwFXNr66\nWSxZdnp4ROuOj6/C6bD2q+YeS2QNp0QGXJVlsacn87MdA66is4cDrsQWQM1wGI/b5fLx2OvVgJFj\nNpyctVAy/5XrZnPJ2qqEtm+0bvvYCry+QL+aXEIIIYZPAi4RtvnYDgD8jdOxNFWxYnZvva0FM4e+\nvYzT3vsFnT3A1j3DZbdZ2bhyenhvxkynsfffWUvKB7xfeITLn9ikeX/AeLzuiKT5oSaYv3uokSO1\nHby5rx6AVWpqQtuWCFaLRYItIYRIEAm4RFitt5qgzcK3r7iagqzsESdLOx29QcdQRsSG4/qLFB3d\nHrbrBu769GoOnmhj7eKyAe8Tqinl9iR2hMsfI4D76+Zq3n9mZb++O3SyjZL8TApzMwgEgtz76C58\nETll02QjaCGEmNAkh2uS21N7jC88/T1u/dudeOytWFwFlBXkj2pl2lhXdL/x0tO459ZzKCvK5pyl\n06Iqz8dyvL4TgN+/sD+h7fDFScJ/4Kl9UdeP1XXw3d9s56ePvAcY2xH1ve9giwKEEEKkNwm4Jrmn\n9m7Dl9FKgACBzgKWZJ+T6iYNKsNpozA3fpJ8X6EiqYksfur2+nlLN8S87c199bR39+7f+Lc3jwNw\npLad7h5veEPvkCVzSxLWLiGEEOOTTClOcq3eJnDA+flXc+7qeag5U2iNKMI5Und9anXU1GIqvX9N\nJc9sO8Zss3hqIvzppUMD3u72+MGcJTx0sjfQe2XHSWZOjU7+/8LVKyGQ2BWUQgghxpfx8Y0oUsLl\n8dAWPEUwYOXy1YspK8rGYU9MknRVeR7TShK3QnE0zllqJNXPKh+8pMVQHTzZNuDtPWa+2O+e209d\nc28A29TeQ1uXJ+rcovyh7ykphBAiPckI1yQUCATYcngfv931V2z5XRR5Z2O3TdzVaDYzpyy0qjAR\nIoOoWEIJ+i9sPxF1vLndTX52DwD/eMUSFlYVyUpAIYSYBCTgmoT+8PYrvN7+NLZ88LcX8bVLPp3q\nJo2pUEJ6IEEBVzAYDI9gxdPjMUpFWCwQuYVjc3sPbq9x3wWVhSnZM1EIIUTyyZTiJHSkzRh18TWV\n49GryXZO7Cktm1mawp+gPKmuIWzhEwrI8rOdlBX1bo3T0ObC7fVjt1nIzx4/VeWFEEKMLQm4JqFW\nr7H3X6V3LZ//0IoUt2bs2WyhgCsxI1xDqecVCrj8gSA2m5W5FUbCvsvtp7vHN+alM4QQQowvMqU4\nwbS5unjoracpm+Ikw9H/x9vZ46bLWYPFm8XXrj07BS1MvkROKfZ4fOw52jzoeaFpQ38ggM1q4avX\nnc4v/rqbbXvrael0h4uxCiGEmBwk4JpgHt7xCtq7DV078HnFgdnJadA4YLMmboTry/dtCU8pLphZ\nyPTSHF5629hq6PZrVtLh8vKzv+wK53D5/UFsVgsWi4V8c2Nqt8dPdp786gkhxGQin/omj8/L0/u2\nUVrkwDJI5fLx7FDXXrCCe/9K1p82m3XLK8K3bd1Vywtv1wAWrr1ifeoamWRWS/yAKxAM8n/PH+C0\nWcWsmD9l0MeKzN9aMLMwHMwBLKwq4sCJVqDvlKJxTl5EzpbdJpXlhRBiMpGAy/THHa+xpf0ZqE91\nS0bJCkFPBoHWqby8pZvr11eGb3rb5yXY1cW/fmIV82cMfTPqdBcKbmJNKZ6o7+T57Sd4fvsJfn3H\neQSDQb714FtMLcri5iuWDPi4wWCQFfNKeez1I1y5zhgxDG2JpI+1EgwGjYDLDOBzIzbybuv09H9A\nIYQQE9agAZdSqgz4DrBca73GPHYP0GX+Ww58QWtdb972L0A+UAg8p7V+3Dy+HLgFOAJMBW7TWqe8\nvPZfdm7lubonweYFC3hPzOPaDcvJcKZnbaTn3jzG0SM2oP8ISo/bGHWZbHWfrANMKdY0dkVd9/gC\nHK3r4GhdB5/1B6KS24PB6Ps77VaqyvP42W0bwn06rSQbh93Kycau8POFRsEin97jS/lbXwghRBIN\nZYTrHOAxjMAqpFNr/XUApdTtwNeALyil1gAbtdaXKaVswF6l1Mta6w7gt8B5WusGpdSPgE8CDyTy\nxYzEMwc3YyvyEOjOJdiTg692Dsf3FXLdRSrVTRuR1zqtBF2t4euBQDAccPR4jemwzIzJNbDZd0rR\n7fXjsFuxWizsP94adW6Xyxu+XH2qg3nTC8LX61pcUecun2dMQUYGsHablcWzitlxsDE8ihWaUjx7\nSTnPvHGMpvaeRL00IYQQaWLQZCWt9aNAR59jX+/zGJ3m5cuAreY5fmAvsEEpNQfI1Dq82+9m4NLR\nNT1B7B6CQQvuXefgObgSglZeeqeGt/fH3ph4vAsFEyvNfKRud2/OUSivKDNNR+9GymKxYLNaCASC\nNLf3cPPdr/D75w8AvVN7ZkwWlaP13qGmqMd5fHN11PXKsthbBZUXG5sohkbP7OaUYlaGne/ceGb4\nshBCiMljVJ/6SqlC4ALgKvPQVGBPxCnt5rFGooO20PGUs9i94HPQdwru3kff49d3nJeaRo2C2xsg\nw2Ejx6xgfvvPtpCVYUfNLKTbDCayJlnABca0Xm1TFwdOGHsgvrD9BB+/YEHvvoZBePjFg1EbS++u\nbubK9XPC10sLh1YgtqzYKHR60gy4IhPrM5w2vvmZNeRkSsAlhBCTyYg/9ZVS+cBPgU9rrUM7+dYD\nkX/255vH6s3LfY8PqqgoG3uCNlTu6wdP/xFLZhfBbqNpVeV5HD3VGxeWliZus+NkCAaDdHR7yMtx\ncuaSabz+bi09Hj89Hj9/31MHGPlM08oLsFjir5JLt9c9FLMq8tl/rJXIGvGlpXl09RhTiEHgmW3H\nou4TCEb3RUO7G4BrL1RsWDWD0tJcYlGzpwCapk7j/OxsZ9TjxOrfidjn6UD6Pfmkz5NP+nx8GE7A\nFf6GVkpNAX4M3K61rlVKXWVOPT4J3Gme4wAWAq9qrduVUt1Kqalmcv055rmDamkZeJPgkTrSdIq3\n2l8EnxNP9WkAnDarKCrgOlXXFl5hlg4aW110dHtZVFXE4spCSvIz++ULZTpsNDZ2xnkE4xezoaEj\n7u3pqig3A4BDETlbB6ubaOlwx72Py+2L6ou9R5qwWS1sWDYNB8G4/ZRhvmVeMetz5WXaB+zTidrn\n4530e/JJnyef9HnyxQtwh7JKcT1wHVCulPoqcA/wLGADfqeUsmBMET6qtX5DKfWSUuq7GKsUb9Na\nt5sP9Qnge0qpaoy8r4dG9YpGIRAI8IMtv8SaAwXe2SxZuITVC6fylo4edPN4A2RljO+Ay+3xY7NZ\n2HW4meYOI7gqLTSmtBz2/m2PzOmaTJxmX5xq6l2V2NbpxjvAakFfxG3BYJBOl5fKsryY/RqpMNdJ\nhsMWrjY/q1z+uhRCiMlu0IBLa/0q8Gqfw6cPcP7dcY6/C9w4rNaNkdcP7cWa00HAnUmxawkfv2wB\nAFt2RZdn9/oCZGWkooVD4/MHuPmeV/odL84zGp2YnQMnBqe5krC2qXfEdKDRLTD6N8Tt9ePzB8nL\ndgz6XBaLhbxsB+42cwPrHNmkWgghJrtJmbn71OGXwAHew8u4zAy2AK5YN4emdjc9Hh+Hatrx+Abf\npDhVgsEg9z+xp9/xRVVFrFxQCsSqxDV5hUa4wkny0K8kRCSb1RIVcHV2G7leOZmDB1zQG+ABFEjA\nJYQQk96kCbi8fh8PvvkMzmw3HY5j2NwF/OSGy8mO+AItzM3gtqtX8NAz+zhU0z7gdFOq7TzUxLa9\nvVOgN1y6iFULSqPKDaxbNo0/vnyIDSsqWDyrGLfXHx79mmycMYq9Pv3GsRhnGgpznbR0ePj5Y7vY\ntHJ6+P5DGeGC3gAPZIRLCCHEJAq4Hnn7DXZ0vwrmjJKlrSIq2IoUKmS582ATv/3bfm65cinZ42wZ\nf1tn9HRYeXF2v9pOF51ZydrF5RRN0iArktMRP++qJD+Dpvbo/szPMY5t21vPtr31fPGjRt3f3Kzh\njXDZbdZJV/dMCCFEf+M7IzxBthzew+bavwPgPTGfnt1rWZgVNw2N4nyj3tLDLx1k79EWXtlRk5R2\nDpU/EGC7ji7MGisQsFosEmyZnAOUFtm0akbU9ZxMOxl9ArRmc7Xn0AOuULFT24AlOIQQQkwO42vY\nZoz8Xj9KINdYLOlvLuPSVUt4/5qZcc8vyY8ucBlrD75UemrrUXYdaQaMqvE9Hj952TJtNRDnACsL\nQ/sfhqaQC3MzcEQEaBbgoWc0MIyAy7x/lnNS/IoJIYQYxIT/NggEAvjtXQRdOXgOruCuazbF3ZIl\npO/oRiA4fgKumsYu/vzaEQCuOX8+Zy0pp8vlHXdTnuNNrByuEAsWfviPZ/PPP3kdgJKCzKjqVUuq\nNgAAE1hJREFU8JE//aEGXKG7T9YyHEIIIaJN+CnFk+2tWGx+gq5cvvqRjYMGW2Dk3UQKjKMRrvqI\nQrDnnT6d3CwHZebefSK+gXK4VGUh+REjhGeoqXFrbVmtQ5sevPSsWQBsWjl96I0UQggxYaXNsEhT\ndyuP7/o7U0ps2KwWggQJBoPm6EPv5dBxMHKddlTXgB0qi6Yyt6JgSM/VN+Dy+lO/WjEQCGKxGMVY\nAd6/pjKtquCnWrwcrk0rp4cXG8yels+R2nbOWFjKvmMtMc+fPW1oRUyryvO470vrwwswhBBCTG7j\nPuDaengfD733KNbcViwWIP6uNLGZr7CqYOgjDXZ79CiG25Paelw+f4Dbf7aF1s7eGlLTS3NS2KL0\nE2+EKzK4vu3qFXh8fjKd9n5BN8CKeVOicrsGkyn5W0IIIUzj/hvhfw4/iC0vQNDjxFM7l8XlM3n/\nmVVYsBgBmHEpvBIsdNmCUWdpu27kA2vn8MFVS4f8nH2/bFMdcD2+uToq2AJk5GSY4o1wRQbX2Zl2\nss1fCUeMgEvKOwghhBipcR9wWawBvLWz8R1fAFiYMn06C4vnD3q/nQcbeettL3ZbIZeuXIp1GNNv\nfb9sO1xegsFgSpb31zV38/iW6n7HB0oCF/3FHeGK877w+vsH2RkScAkhhBihtEgC8jdMJ7RRTafL\nO6T7/O65/UBoGmh4L7PvCNe7h5rYsuvUsB4jUY7X986hFub2JnZLaafhiT/CFfu90enqv7pQRhWF\nEEKM1LgPuJzuUhaU9ham7Bni9F6HGZjdeNlpw37OWF/CkYFPMp1o6H3eqrI8fnjz2XxowxwWzypO\nSXvSVeQIV2RF/lhThwAtHT39jknAJYQQYqTGfcD1ySUf4isfX8X9t28CwOMdPODyBwK4PX4WVhaO\naOrNYesdPvrUxQsB6O5JTD0ll9vHf/5xJz/437d5+MWDg55/qKYtfLnL7aOkIJNLz5o15PIEwhA5\nwpUdEXDZbbH7saLEWJQQWXJDcriEEEKM1LjP4Voxcw5g1D9y2K1DGuFyuY1z4u2VOJjIIK2kwKg6\nn6gClt984E3qW10A7DvWypXr58Sd8nxt50l2V/eWJ8jJGPc/rnHLEWeEK9ZqRIBrL1jA4tnFZDrt\n/OSRdwHJ4RJCCDFyafUNnuGw4R7CCNfRug4ACnJGtt1N5JdwaJufrgFyx9q6PORm2YdUFysUbEVe\nnz6lf4mHYDDIA0/vA+DC1cY2RBevrRq88SIma0TSW1V5Lm6vD7c3wOxp+THPz8qws3ZxOdWn2sPH\nZEpRCCHESKVdwDWUKcUXt58AYOmcklE/Z26Wg+L8DE5FVHiP9OTWah555TBqZiFfvmZl3Km+TpeX\nv715vN/xnjgjZ/UtvYHZ1efNkw2QE8BigWAQrj5v/pC36CnI6d38W6YUhRBCjFR6BVxOG+1dnri3\nu9w+7DYLu6ubmVKQyYr5U0b9nFkZNsqKstl7tAWfP9BvCuo5M4jSx1vZtreOtYvLYz7OC9tP8IRZ\n3mH2tDyWzinhr5urwxsm96WPtwLwiQsXSLCVIPffvmnYfZmX3RuYyZSiEEKIkUqvgMthjTuluPm9\nWn715N7w9VULSkf1XFMKMmls68FmtYZzrGIFXP6IfRYb+kwXRjp4ojV8uaQgK/yYnjgB134z4Fow\ns3BkL0D0M5LANfLnLVOKQgghRirNAi4bXl+AQCDInupmmjvcrF9eAcAbe+qizl0+d3TTid/97JkE\nzFjIZk4THjjRRkOri2nF2djtVubPKCQYsa+1K05Cvz8Q4NDJ3lygKfmZ4VVzfUe4DtW08eun9lLf\n4iLDYaMiRn6XSA0JuIQQQoxUWgVcodWDje093PPwTgDOXFRGhtPGqeboHKv5oxwZitwzLzTK8WPz\nOUN+fcd5uL1+sjLsuNw+XHHysU41u6JWVxbmOsMjXH0rmj+xpZraJuO1VJXnRCV7i9SSUhxCCCFG\natzX4YoUGmG44+dbw8dONXfjcvtoaustVLmoqijucv+RiFeryecP4A8EwxXg+wZcu4408eX7Nofz\nvM5aXMaV6+dw7rKKcMDl6vGFpw8BphRkhS+HakGJ1DrLzMsLrVgVQgghhiutRrj6jmKBUR/r6KkO\nImb2OHtJ7MT1kbLFCd5efqcGMFYy2m0WTjZGt++hp/fR1O7m1Z0nAVhUVcy5y6YBhAOu//mbsQXR\nNz69msqyvKgctXiBnkiuGy5bxA2XLpIRLiGEECOWVgHX+86YwQNP7Ys65vMHOGFuu3PT5YupKMlh\nxtTcpLTnf58/AEBxfiY+f5BjZv2vELc3Oj8rOzNiS5k+xU6bO9xUluWF94rMcNo4//QZiNSzWiyh\nrTyFEEKIEUmrKcV1yyr4yRfWRR3z+gIcrjUS0udWFIxJsPXe4aYBb7/0rCqyM+34A8GoJHivPzrg\nitzPr6woO+o2t5nj1dblxm6zct8X1zO9NDmBoxBCCCHGVloFXBA9SgRGBfjtuoH8HCfF+Rlx7jU6\nzjhb7wBsXFHBjNJcsswaTS6PkcfV3eMNB1Ehkavcyouzo66H7tfa6aEw1ym1t4QQQogJJO0Crr6r\n9kLb37R3ecYsSPH5g3Fvs5vBWKgg6x9eMDakfvDpff3OjQywrFYLqrJ3JWUo4b6rx0vOEKugCyGE\nECI9pF3AFc/7xjDfaaDthEK5WKHE+q27T3GsriNc2iHWuSG3XrWUmy5fDMCppm6CwSBeb2DAETUh\nhBBCpJ+0/Gb/+qfOYNPK6VHHPnDOrDF7vqvWz4l7W6iA6WcuWUSOOd3Z5fLS3NHDjNLosg59Vzva\nbVZOV6UU5jrZsusU1eZqSwm4hBBCiIklLb/ZZ5XnM29GQdSxoW5GPBKbVs3gv7+8kbnT8zlv1XSm\nFvbWygqNWpUUZHLx2ioA2ru9uNx+iiPqNn3g7FlR9wuxWa187Pz5+ANBvv3QW+ZjSkVzIYQQYiJJ\nq7IQkSKLgi6bWzLmSeZ2m5WvXXcGAA+/eJBnth0DiNpM22GOYNW1GNOJxfmZXLluNplOOxesnhn3\nsfuuRrTLCJcQQggxoaRtwFVVnseiqiL2Hm2hrdMz+B0SaHZFfvhyTWNX+HIoUGrtcAPGqNsHzpk9\n6OPl9Fl5KVOKQgghxMSS1t/sN12+mOVzS/jEhQuS+ryzp+WFL19iTiNC7whXt7nicKiBU9+Ayx+I\nvypSCCGEEOknbUe4APKynXzhI8uT/ryRe+otqioKXw7lc3X3DC/g6puzFW8TbCGEEEKkp7QOuFLF\nYrHw7RvW9MsbC22YHQqY+paBGMhlZ1fxxJajZDhsfHAI05BCCCGESB8ScI1QrG13QgFWlznCNZzk\n9yvWzWHjiulRKxuFEEIIMTGkdQ7XeJOTZcSvLZ1G0vxwRrisFosEW0IIIcQEJQFXAhXkOIHejagd\nNqmnJYQQQggJuBKqICeDyLSu4YxwCSGEEGLikogggRx2K6UFvdXkSwtlilAIIYQQEnAl3LJ5JQC8\n74wZTCvJGeRsIYQQQkwGskoxwS4/dzYLZhSyakFpqpsihBBCiHFCAq4Ey8l0cMbCqaluhhBCCCHG\nkUEDLqVUGfAdYLnWeo15rAj4PnAYmAd8VWvdYN72L0A+UAg8p7V+3Dy+HLgFOAJMBW7TWgcS/oqE\nEEIIIcaZoeRwnQM81ufY9zCCqX83b7sbQCm1Btiotf468EXgbqVUaOPB3wJf01r/G+AHPpmA9gsh\nhBBCjHuDBlxa60eBjj6HLwW2mpc3A5eYly8LHdda+4G9wAal1BwgMzQKZt7n0tE1XQghhBAiPYw0\nh2sqvUFYO1CklLKax/dEnNduHmskOmgLHR9UaWmeZfCzRCKVluYNfpJIKOnz1JB+Tz7p8+STPh8f\nRloWog4I/QTzgRYzH6s+4njotnrzX36M40IIIYQQE95wAq7IkaYngbPMy+ea16OOK6UcwELgVa31\nYaBbKRUa1Ton4j5CCCGEEBOaJRgMDniCUmo9cD1wEfAzjAT5bIxViseAOcAdEasUbwOKMVYpPq21\nfsI8vgz4PFBt3v4vskpRCCGEEJPBoAGXEEIIIYQYHdnaRwghhBBijEnAJYQQQggxxiTgEkIIIYQY\nYxJwCSGEEEKMMQm4hEgCpVRpqtsw2SilqlLdhslGKTU91W2YbJRSi5RSualuhxicrFKcRJRSWcAP\ngWe01k8opWzmFkxijJh9fhXGVlZ7gT9orfentlUTm1IqB6O/rwP2A89qrf+W2lZNfEqp64E7gK9q\nrf+ilLJK6Z+xo5QqBL4KbABu0FrvSnGTxCBkhGtyWQrMxNxsXGvtV0rJ1kljRCl1OnAvRu25r2EU\n/F2RyjZNdEqpxcB/Aw3A54ASYLp5m7zXx4C5rRsY3ydvAR9WSuVqrQPS52NDKbUceArjj7izgYMR\nPwcxTskPaBKI+NArBG4AmpRSXzOPyXtg7DQDR4AtWusjwGsYQa8YO0eABq31S1rrWqAF8JsBgAzn\nJ5hSyhIxilWG8R7vAK5VSpVIn4+Z/cDLwBvAjRh/0N2nlPpoKhslBiZfthOUUmq2UupzSqn8iA+9\nLVrrRuCbwOeVUlPMUS55HyRARJ8XmIdqgXsj+v8Axoek5HQlSOT7HEBr3Q18w7xtHcauFouAh5RS\nm1LW0Amk72eLUspmbuVWAzwEHAZuAr5kni+jXKMU433uAl7B2L3lWa31ncAe4CqllIyij1PyRTsB\nKaWcwO0YuUOXh45rrTvN/5/F+OL/rnlTdpKbOOHE6nOtdY/WujXitEXAZqXUIuCa5LdyYhngfR7q\n8x1a608Cd2GMCJQkvZETTJz3uV9r7cWY2roWKAV2Azal1AIZ5RqdAd7nzwI/0VpXm4f+AnQDrmS3\nUQyNBFwTkxN4F3geOEspNRuici0A/hX4rFLqAYypADE6kX2+tm+fm6uICjA+MH8BuOUv/1GL9z4P\n9WuO+f9qYCGwL+ktnHji9XkJxgjXKYzprT3AR4B18j4ftZh9DqC13qOUmmdenY7xnu9IfhPFUMgq\nxQlAKbUQ46+f54E9WutOpZQNWA7cbB77sXmuBcjA2Iz8YuDHWmudmpanr+H0uXn+5cBvgF8Bv9Ja\n705Bs9PaMN/nWRjTWsswRnNf1FofT0nD09gw+7xIa91iTqlfBryhtT6YqranqxF8nn8LmAO8hLEC\n/URqWi4GIwFXmjKTVYNKqZuBfIxk4Q1Ajtb6UxHnfR5YghFY7TWP2YF8rXVz8luevkbZ5+cAM7XW\n/5f8lqevUfb5fKBUa70l+S1PXyPo8//QWu/pe/8kNzutjfJ9PhOo0Fq/kfyWi+GQKcU0ZP5Vk2Ve\nLcT4q+Zh4OvAh5RSF0ac/kfAA9ytlLpFKeXUWvsk2BqeBPT5Zgm2hmeUfZ6htT4gwdbwjLDPfxh6\nnwNIsDU8CXifH5dgKz1IwJVmlFJnAfcAPzJrDm0AqgC01k0YCcI/iriLDSNH6x3gN1prT3JbnP6k\nz5MvAX3uTm6L098o+nwH8j4fEXmfTy4ypZgmzKTrb2CUFngYuB/jl64VuFVrvdA8LwP4M/AVrfV7\nSqkpQKbM6w+f9HnySZ8nn/R58kmfT04ywpU+ghirgJ7XWrdg1NJao7W+F/Appf7ZPK8EYzXWHgCt\ndaP8co6Y9HnySZ8nn/R58kmfT0L2VDdADFk38Kc+K622mv/fCbxfKfVDoB3YrmWPxESQPk8+6fPk\nkz5PPunzSUgCrjRhJqJG/nJWYeyjBUadlu9hFBw8Ys79i1GSPk8+6fPkkz5PPunzyUkCrvQ1DWhW\nSv0ecAN/01ofTXGbJjrp8+STPk8+6fPkkz6fBCRpPg0ppcqBLcB7wMNa69+luEkTnvR58kmfJ5/0\nefJJn08eMsKVngIYFct/JMuCk0b6PPmkz5NP+jz5pM8nCRnhEkIIIYQYY1IWQgghhBBijEnAJYQQ\nQggxxiTgEkIIIYQYYxJwCSGEEEKMMQm4hBBCCCHGmARcQgghhBBjTOpwCSHSnlLqbOA7wGnAo0Ax\nkAM8qLV+ZJD7fhLYqLX+9Jg3VAgxackIlxAi7WmttwAPAYe11v+otf4Y8FngTqXUF4bwEFKQUAgx\npqTwqRBiQjBHqv5Ba312xLELgUcwRr7uA3YBJcB2rfUvlFLzgJ8BFcALwONa6+eUUv8EzAdcQCHw\nRa11d1JfkBBiQpEpRSHERPYmkA1Mxdg65RUApdROpdRjWuuDSqnfAhu01p83bzsP+KDW+gLz+reB\nrwB3peQVCCEmBAm4hBATnQUjfWKTUuoajFGrImAucCrG+RcDJUqp+8z7lgC1SWqrEGKCkoBLCDGR\nrQE6gfOBFVrrKwCUUisA2wD326q1viV0RSmVPaatFEJMeJI0L4SYkJRS5cC/Y0wFlgDN5nErMCPi\n1B7M4EspdT3wDMZomNU8dgUwlMR7IYSIS5LmhRBpTym1Fvg2sBj4E0ZZiHzgN1rrPymlZgK/Bw5g\nBF4fBN4FbsDI8foTcBB4UWv9oFLq88DZwHEgE7hNa+1J7qsSQkwkEnAJIYQQQowxmVIUQgghhBhj\nEnAJIYQQQowxCbiEEEIIIcaYBFxCCCGEEGNMAi4hhBBCiDEmAZcQQgghxBiTgEsIIYQQYoz9f5PT\n7h3vLMzDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131887410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6));\n",
    "spxvix['SPX'].plot(label='S&P 500');\n",
    "spxvix['SPX'].cummax().plot(label='running maximum');\n",
    "plt.legend(loc=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "264.38000499999998"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adrawdown = spxvix['SPX'].cummax() - spxvix['SPX']\n",
    "adrawdown.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19388242085950988"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdrawdown = (spxvix['SPX'].cummax() - spxvix['SPX']) / spxvix['SPX'].cummax() \n",
    "rdrawdown.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(301)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = adrawdown[adrawdown == 0]\n",
    "periods_spx = (temp.index[1:].to_pydatetime()\n",
    "             - temp.index[:-1].to_pydatetime())\n",
    "periods_spx[50:60]  # some selected data points\n",
    "\n",
    "max(periods_spx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:team@tpq.io\">team@tpq.io</a>\n",
    "\n",
    "**DX Analytics** |\n",
    "<a href=\"http://dx-analytics.com\">http://dx-analytics.com</a>\n",
    "\n",
    "**Quant Platform** |\n",
    "<a href=\"http://quant-platform.com\">http://quant-platform.com</a>\n",
    "\n",
    "**Python for Finance Books** |\n",
    "<a href=\"http://books.tpq.io\" target=\"_blank\">http://books.tpq.io</a>\n",
    "\n",
    "**Python for Finance Training** |\n",
    "<a href=\"http://training.tpq.io\" target=\"_blank\">http://training.tpq.io</a>"
   ]
  }
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